diff --git a/.gitignore b/.gitignore index b14fbc0..d51b54a 100644 --- a/.gitignore +++ b/.gitignore @@ -44,6 +44,9 @@ docs/_rst/* docs/_build/* cover/* MANIFEST +**/runs/** +**/logs/fit/** + # Per-project virtualenvs .venv*/ diff --git a/examples/ODE_Example_coupled_nonlin.ipynb b/examples/ODE_Example_coupled_nonlin.ipynb index b67a7d7..ea23ec2 100644 --- a/examples/ODE_Example_coupled_nonlin.ipynb +++ b/examples/ODE_Example_coupled_nonlin.ipynb @@ -11,31 +11,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook we provide a simple example of the DeepMoD algorithm by applying it on the Burgers' equation. \n", + "In this notebook we provide a simple example of the DeepMoD algorithm by applying it on a simple ODE\n", "\n", "We start by importing the required libraries and setting the plotting style:" ] }, { "cell_type": "code", - "execution_count": 287, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# General imports\n", "import numpy as np\n", "import torch\n", "import matplotlib.pylab as plt\n", "# DeepMoD stuff\n", + "import sys\n", + "sys.path.append('../src/')\n", "from deepymod_torch.DeepMod import DeepMod\n", "from deepymod_torch.training import train_deepmod, train_mse\n", "from deepymod_torch.library_functions import library_1D_in\n", @@ -54,18 +47,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we prepare the dataset." + "Next, we prepare the dataset. The set of ODEs we consider here are\n", + "$d[y, z]/dt = [z, -z- 5 \\sin y]$" ] }, { "cell_type": "code", - "execution_count": 288, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "def dU_dt_sin(U, t):\n", - " # Here U is a vector such that y=U[0] and z=U[1]. This function should return [y', z']\n", + "def dU_dt_true(U):\n", + " \"\"\"\n", + " returns the right hand side of the differential equation\"\"\"\n", " return [U[1], -1*U[1] - 5*np.sin(U[0])]\n", + "\n", + "\n", + "def dU_dt_sin(U, t):\n", + " \"\"\"\n", + " returns the right hand side of the differential equation\"\"\"\n", + " return dU_dt_true(U)\n", "U0 = [2.5, 0.4]\n", "ts = np.linspace(0, 8, 500)\n", "Y = odeint(dU_dt_sin, U0, ts)\n", @@ -81,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 347, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -98,19 +99,17 @@ }, { "cell_type": "code", - "execution_count": 395, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -125,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 396, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 397, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -162,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 355, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -175,12 +174,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we show an example where we create a custom library. $\\theta$ in this case containe $[1,u,v, sin(u),cos(u)]$ to showcase that non-linear terms can easily be added to the library" + "Here we show an example where we create a custom library. $\\theta$ in this case containe $[1,u,v, cos(u),sin(u)]$ to showcase that non-linear terms can easily be added to the library" ] }, { "cell_type": "code", - "execution_count": 398, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 399, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 400, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 401, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -270,25 +269,21 @@ "output_type": "stream", "text": [ "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 3100 6.20% 430s 1.53e-02 1.12e-02 4.06e-03 3.81e-05 " - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_deepmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'l1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1e-5\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Documents/GitHub/New_DeepMod_Simple/DeePyMoD_torch/src/deepymod_torch/training.py\u001b[0m in \u001b[0;36mtrain_deepmod\u001b[0;34m(model, data, target, optimizer, max_iterations, loss_func_args)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;34m'''Performs full deepmod cycle: trains model, thresholds and trains again for unbiased estimate. Updates model in-place.'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Train first cycle and get prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iterations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_func_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_deriv_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse_theta_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeff_vector_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/GitHub/New_DeepMod_Simple/DeePyMoD_torch/src/deepymod_torch/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, data, target, optimizer, max_iterations, loss_func_args)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0mboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0mexp_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;31m# Maintains the maximum of all 2nd moment running avg. till now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + " 50000 100.00% 0s 4.16e-04 1.04e-04 1.21e-04 1.91e-04 \n", + "[Parameter containing:\n", + "tensor([[1.1431]], requires_grad=True), Parameter containing:\n", + "tensor([[ 8.3063],\n", + " [-12.3521]], requires_grad=True)]\n", + "[tensor([2]), tensor([1, 4])]\n", + "\n", + "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", + " 50000 100.00% 0s 2.87e-01 8.45e-05 2.87e-01 0.00e+00 CPU times: user 9min 43s, sys: 2.2 s, total: 9min 45s\n", + "Wall time: 9min 45s\n" ] } ], "source": [ + "%%time\n", "train_deepmod(model, X_train, y_train, optimizer, 50000, {'l1': 1e-5})" ] }, @@ -301,133 +296,256 @@ }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "solution = model(X_train)[0].detach().numpy()" + "solution = model(X_train)[0].detach().numpy()\n", + "solution_derivatives = np.transpose(np.array([timeder.detach().numpy().squeeze() for timeder in model(X_train)[1]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can perform some extra debugging by making use of class operations" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, torch.Size([400, 5]))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_deriv, theta = model.library((model.network(X_train),X_train))\n", + "len(time_deriv), theta.shape" ] }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([2.52496066, 2.88267006])" + "(400,)" ] }, - "execution_count": 328, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.max(np.abs(Y),axis=0)" + "time_deriv[0].detach().numpy().squeeze().shape" ] }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Parameter containing:\n", - "tensor([[-0.0083],\n", - " [ 0.1683],\n", - " [ 0.3555],\n", - " [-0.4266],\n", - " [ 0.9913]], requires_grad=True) Parameter containing:\n", - "tensor([[ 0.0119],\n", - " [-2.6910],\n", - " [-0.6931],\n", - " [ 0.4714],\n", - " [-0.4228]], requires_grad=True)\n" + "time deriv 0.5150814\n", + "\t 2 1.1431264877319336\n", + "\t 0.5147162416952256\n", + "time deriv 0.3614447\n", + "\t 1 8.306324005126953\n", + "\t -1.6173576534190488\n", + "\t 4 -12.352091789245605\n", + "\t 2.389956342350686\n" ] } ], "source": [ - "print(model.fit.coeff_vector[0],model.fit.coeff_vector[1])" + "for i, (sparse, coeff_vector) in enumerate(zip(model.fit.sparsity_mask,model.fit.coeff_vector)):\n", + " print('time deriv', time_deriv[i].detach().numpy().squeeze()[0])\n", + " for sparse_element, coeff in zip(sparse.detach().numpy(),coeff_vector.detach().numpy()):\n", + " print('\\t',sparse_element, coeff.item())\n", + " print('\\t',theta[0,sparse_element].detach().numpy()*coeff.item())" ] }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "([tensor([2]), tensor([1, 4])],\n", + " Parameter containing:\n", + " tensor([[1.1431]], requires_grad=True),\n", + " Parameter containing:\n", + " tensor([[ 8.3063],\n", + " [-12.3521]], requires_grad=True))" ] }, - "execution_count": 330, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" - }, + } + ], + "source": [ + "model.fit.sparsity_mask, model.fit.coeff_vector[0], model.fit.coeff_vector[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\theta$ in this case contains $[1,u,v, cos(u),sin(u)]$. The form of the discovered equation is" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "u_t = +[1.1431265]+v \t\n", + "v_t = +[8.306324]+u +[-12.352092]+sin(u) \t\n" + ] + } + ], + "source": [ + "library = ['','+u','+v','+cos(u)','+sin(u)']\n", + "ders = ['u_t','v_t']\n", + "for sparse, coeff_vector, der in zip(model.fit.sparsity_mask,model.fit.coeff_vector,ders):\n", + " expression = ''\n", + " for sparse_element, coeff in zip(sparse.detach().numpy(),coeff_vector.detach().numpy()):\n", + " expression += ' +'+str(coeff)+library[sparse_element]\n", + " print(der,' =',expression, '\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We check that the model consistently evaluates the derivatives:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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" + "" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "plt.scatter(X_train.detach().numpy().squeeze(),solution[:,0])\n", - "plt.plot(T_rs,Y_rs[:,0])" + "def dU_dt_estimate(U):\n", + " TH = [1, U[0], U[1], np.cos(U[0]), np.sin(U[0])]\n", + " output = []\n", + " for sparse, coeff_vector in zip(model.fit.sparsity_mask,model.fit.coeff_vector):\n", + " expression = 0\n", + " for sparse_element, coeff in zip(sparse.detach().numpy(),coeff_vector.detach().numpy()):\n", + " expression += coeff.item()*TH[sparse_element]\n", + " output.append(expression)\n", + " # Here U is a vector such that u=U[0] and v=U[1]. This function should return [u', v']\n", + " return output #[1.1369082*U[1], 2.9121633 + (-2.9401846)*np.cos(U[0]) +(-10.048162)*np.sin(U[0])]\n", + "\n", + "result = np.array(list(map(dU_dt_estimate, y_train)))\n", + "deriv_eq_true = np.array(list(map(dU_dt_true, y_train)))\n", + "\n", + "plt.scatter(X_train.detach().numpy().squeeze(),result[:,0],label = 'udot via discovered equation')\n", + "plt.scatter(X_train.detach().numpy().squeeze(),result[:,1],label = 'vdot via discovered equation')\n", + "#plt.scatter(X_train.detach().numpy().squeeze(),solution_derivatives[:,0],s=3) # this line is equivalent to the subsequent one\n", + "plt.scatter(X_train.detach().numpy().squeeze(),time_deriv[0].detach().numpy().squeeze(),s=25, label = 'udot from DeepMoD autodiff', marker='x')\n", + "\n", + "#plt.scatter(X_train.detach().numpy().squeeze(),solution_derivatives[:,1],s=3) # this line is equivalent to the subsequent one\n", + "plt.scatter(X_train.detach().numpy().squeeze(),time_deriv[1].detach().numpy().squeeze(),s=25, label = 'vdot from DeepMoD autodiff', marker='x')\n", + "\n", + "\n", + "plt.scatter(X_train.detach().numpy().squeeze(), deriv_eq_true[:,0], label='u_dot true', marker='o', s=6)\n", + "plt.scatter(X_train.detach().numpy().squeeze(), deriv_eq_true[:,1], label='v_dot true', marker='o', s=6)\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks confusing because the curves don't match" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we test if the prediction of the model matches the actual rescaled training data" ] }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "" ] }, - "execution_count": 331, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "plt.scatter(X_train.detach().numpy().squeeze(),solution[:,1])\n", - "plt.plot(T_rs,Y_rs[:,1])" + "plt.scatter(X_train.detach().numpy().squeeze(),solution[:,0],label='DeepMod u')\n", + "plt.scatter(T_rs,Y_rs[:,0],s=3, label='Scaled u')\n", + "plt.scatter(X_train.detach().numpy().squeeze(),solution[:,1], label='DeepMod v')\n", + "plt.scatter(T_rs,Y_rs[:,1],s=3, label='Scaled v')\n", + "plt.legend()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -452,7 +570,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/examples/ODE_Example_coupled_nonlin_no_norm.ipynb b/examples/ODE_Example_coupled_nonlin_no_norm.ipynb new file mode 100644 index 0000000..b178b7a --- /dev/null +++ b/examples/ODE_Example_coupled_nonlin_no_norm.ipynb @@ -0,0 +1,505 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example ODE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebooks addresses the shortcomings of the notebook with a similar name which have to do with applying normalization\n", + "\n", + "We start by importing the required libraries and setting the plotting style:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# General imports\n", + "import numpy as np\n", + "import torch\n", + "import matplotlib.pylab as plt\n", + "# DeepMoD stuff\n", + "import sys\n", + "sys.path.append('../src/')\n", + "from deepymod_torch.DeepMod import DeepMod\n", + "from deepymod_torch.training import train_deepmod, train_mse\n", + "from deepymod_torch.library_functions import library_1D_in\n", + "\n", + "from scipy.integrate import odeint\n", + "\n", + "# Settings for reproducibility\n", + "np.random.seed(40)\n", + "torch.manual_seed(0)\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we prepare the dataset. The set of ODEs we consider here are\n", + "$d[y, z]/dt = [z, -z- 5 \\sin y]$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def dU_dt_true(U):\n", + " \"\"\"\n", + " returns the right hand side of the differential equation\"\"\"\n", + " return [U[1], -1*U[1] - 5*np.sin(U[0])]\n", + "\n", + "\n", + "def dU_dt_sin(U, t):\n", + " \"\"\"\n", + " returns the right hand side of the differential equation\"\"\"\n", + " return dU_dt_true(U)\n", + "U0 = [2.5, 0.4]\n", + "ts = np.linspace(0, 8, 500)\n", + "Y = odeint(dU_dt_sin, U0, ts)\n", + "T = ts.reshape(-1,1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can potentially rescale the Y and T axis and we plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "T_rs = T\n", + "Y_rs = Y #Y/np.max(np.abs(Y),axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot it to get an idea of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(T_rs, Y_rs[:,0])\n", + "ax.plot(T_rs, Y_rs[:,1])\n", + "ax.set_xlabel('t')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "number_of_samples = 400\n", + "\n", + "idx = np.random.permutation(Y.shape[0])\n", + "X_train = torch.tensor(T_rs[idx, :][:number_of_samples], dtype=torch.float32, requires_grad=True)\n", + "y_train = torch.tensor(Y_rs[idx, :][:number_of_samples], dtype=torch.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([400, 1]) torch.Size([400, 2])\n" + ] + } + ], + "source": [ + "print(X_train.shape, y_train.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup a custom library" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.autograd import grad\n", + "from itertools import combinations, product\n", + "from functools import reduce" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we show an example where we create a custom library. $\\theta$ in this case containe $[1,u,v, cos(u),sin(u)]$ to showcase that non-linear terms can easily be added to the library" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def library_non_linear_ODE(input, poly_order, diff_order):\n", + " \n", + " prediction, data = input\n", + " samples = prediction.shape[0]\n", + " \n", + " # Construct the theta matrix\n", + " C = torch.ones_like(prediction[:,0]).view(samples, -1)\n", + " u = prediction[:,0].view(samples, -1)\n", + " v = prediction[:,1].view(samples, -1)\n", + " theta = torch.cat((C, u, v, torch.cos(u), torch.sin(u)),dim=1)\n", + "\n", + " # Construct a list of time_derivatives \n", + " time_deriv_list = []\n", + " for output in torch.arange(prediction.shape[1]):\n", + " dy = grad(prediction[:,output], data, grad_outputs=torch.ones_like(prediction[:,output]), create_graph=True)[0]\n", + " time_deriv = dy[:, 0:1]\n", + " time_deriv_list.append(time_deriv)\n", + " \n", + " return time_deriv_list, theta\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuring DeepMoD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now setup the options for DeepMoD. The setup requires the dimensions of the neural network, a library function and some args for the library function:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "## Running DeepMoD\n", + "config = {'n_in': 1, 'hidden_dims': [40, 40, 40, 40, 40, 40], 'n_out': 2, 'library_function': library_non_linear_ODE, 'library_args':{'poly_order': 1, 'diff_order': 0}}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we instantiate the model. Note that the learning rate of the coefficient vector can typically be set up to an order of magnitude higher to speed up convergence without loss in accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "model = DeepMod(**config)\n", + "optimizer = torch.optim.Adam([{'params': model.network_parameters(), 'lr':0.001}, {'params': model.coeff_vector(), 'lr':0.005}])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run DeepMoD " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now run DeepMoD using all the options we have set and the training data. We need to slightly preprocess the input data for the derivatives:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", + " 50000 100.00% 0s 5.70e-05 1.18e-05 1.81e-05 2.72e-05 \n", + "[Parameter containing:\n", + "tensor([[1.0002]], requires_grad=True), Parameter containing:\n", + "tensor([[-0.9994],\n", + " [-4.9970]], requires_grad=True)]\n", + "[tensor([2]), tensor([2, 4])]\n", + "\n", + "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", + " 50000 100.00% 0s 4.05e-05 1.69e-05 2.36e-05 0.00e+00 CPU times: user 9min 43s, sys: 7.32 s, total: 9min 50s\n", + "Wall time: 9min 49s\n" + ] + } + ], + "source": [ + "%%time\n", + "train_deepmod(model, X_train, y_train, optimizer, 50000, {'l1': 1e-5})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that DeepMoD has converged, it has found the following numbers:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "solution = model(X_train)[0].detach().numpy()\n", + "solution_derivatives = np.transpose(np.array([timeder.detach().numpy().squeeze() for timeder in model(X_train)[1]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, torch.Size([400, 5]))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_deriv, theta = model.library((model.network(X_train),X_train))\n", + "len(time_deriv), theta.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\theta$ in this case contains $[1,u,v, cos(u),sin(u)]$. The form of the discovered equation is" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "u_t = +[1.0001938]+v \t\n", + "v_t = +[-0.99937963]+v +[-4.9969854]+sin(u) \t\n" + ] + } + ], + "source": [ + "library = ['','+u','+v','+cos(u)','+sin(u)']\n", + "ders = ['u_t','v_t']\n", + "for sparse, coeff_vector, der in zip(model.fit.sparsity_mask,model.fit.coeff_vector,ders):\n", + " expression = ''\n", + " for sparse_element, coeff in zip(sparse.detach().numpy(),coeff_vector.detach().numpy()):\n", + " expression += ' +'+str(coeff)+library[sparse_element]\n", + " print(der,' =',expression, '\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This corresponds to the right ODE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We check that the model consistently evaluates the derivatives:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def dU_dt_estimate(U):\n", + " TH = [1, U[0], U[1], np.cos(U[0]), np.sin(U[0])]\n", + " output = []\n", + " for sparse, coeff_vector in zip(model.fit.sparsity_mask,model.fit.coeff_vector):\n", + " expression = 0\n", + " for sparse_element, coeff in zip(sparse.detach().numpy(),coeff_vector.detach().numpy()):\n", + " expression += coeff.item()*TH[sparse_element]\n", + " output.append(expression)\n", + " # Here U is a vector such that u=U[0] and v=U[1]. This function should return [u', v']\n", + " return output #[1.1369082*U[1], 2.9121633 + (-2.9401846)*np.cos(U[0]) +(-10.048162)*np.sin(U[0])]\n", + "\n", + "result = np.array(list(map(dU_dt_estimate, y_train)))\n", + "deriv_eq_true = np.array(list(map(dU_dt_true, y_train)))\n", + "\n", + "plt.scatter(X_train.detach().numpy().squeeze(),result[:,0],label = 'udot via discovered equation')\n", + "plt.scatter(X_train.detach().numpy().squeeze(),result[:,1],label = 'vdot via discovered equation')\n", + "#plt.scatter(X_train.detach().numpy().squeeze(),solution_derivatives[:,0],s=3) # this line is equivalent to the subsequent one\n", + "plt.scatter(X_train.detach().numpy().squeeze(),time_deriv[0].detach().numpy().squeeze(),s=25, label = 'udot from DeepMoD autodiff', marker='x')\n", + "\n", + "#plt.scatter(X_train.detach().numpy().squeeze(),solution_derivatives[:,1],s=3) # this line is equivalent to the subsequent one\n", + "plt.scatter(X_train.detach().numpy().squeeze(),time_deriv[1].detach().numpy().squeeze(),s=25, label = 'vdot from DeepMoD autodiff', marker='x')\n", + "\n", + "\n", + "plt.scatter(X_train.detach().numpy().squeeze(), deriv_eq_true[:,0], label='u_dot true', marker='o', s=6)\n", + "plt.scatter(X_train.detach().numpy().squeeze(), deriv_eq_true[:,1], label='v_dot true', marker='o', s=6)\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks confusing because the curves don't match" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we test if the prediction of the model matches the actual rescaled training data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_train.detach().numpy().squeeze(),solution[:,0],label='DeepMod y')\n", + "plt.scatter(T_rs,Y_rs[:,0],s=3, label='Scaled y')\n", + "plt.scatter(X_train.detach().numpy().squeeze(),solution[:,1], label='DeepMod z')\n", + "plt.scatter(T_rs,Y_rs[:,1],s=3, label='Scaled y')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.17" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/ODE_Lotka_Volterra.ipynb b/examples/ODE_Lotka_Volterra.ipynb index 247d14a..232a869 100644 --- a/examples/ODE_Lotka_Volterra.ipynb +++ b/examples/ODE_Lotka_Volterra.ipynb @@ -18,15 +18,26 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# General imports\n", "import numpy as np\n", "import torch\n", "import matplotlib.pylab as plt\n", "# DeepMoD stuff\n", + "import sys\n", + "sys.path.append('../src/')\n", "from deepymod_torch.DeepMod import DeepMod\n", "from deepymod_torch.training import train_deepmod, train_mse\n", "from deepymod_torch.library_functions import library_1D_in\n", @@ -41,6 +52,27 @@ "%autoreload 2" ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import deepymod_torch as dpymod\n", + "dpymod" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -99,14 +131,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -215,16 +245,16 @@ "output_type": "stream", "text": [ "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 25000 100.00% 0s 4.12e-04 2.28e-04 1.08e-04 7.62e-05 \n", + " 25000 100.00% 0s 8.28e-05 5.26e-07 6.32e-06 7.59e-05 \n", "[Parameter containing:\n", - "tensor([[ 0.9984],\n", - " [-2.3218]], requires_grad=True), Parameter containing:\n", - "tensor([[-1.4956],\n", - " [ 3.0370]], requires_grad=True)]\n", + "tensor([[ 0.9975],\n", + " [-2.3217]], requires_grad=True), Parameter containing:\n", + "tensor([[-1.4957],\n", + " [ 3.0373]], requires_grad=True)]\n", "[tensor([2, 3]), tensor([1, 3])]\n", "\n", "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 25000 100.00% 0s 2.75e-04 1.72e-04 1.03e-04 0.00e+00 " + " 25000 100.00% 0s 7.89e-06 4.42e-07 7.45e-06 0.00e+00 " ] } ], @@ -232,6 +262,13 @@ "train_deepmod(model, X_train, y_train, optimizer, 25000, {'l1': 1e-5})" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -241,7 +278,77 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "solution = model(X_train)[0].detach().numpy()\n", + "solution_derivatives = np.transpose(np.array([timeder.detach().numpy().squeeze() for timeder in model(X_train)[1]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, torch.Size([250, 4]))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_deriv, theta = model.library((model.network(X_train),X_train))\n", + "len(time_deriv), theta.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "#plt.scatter(X_train.detach().numpy().squeeze(),solution_derivatives[:,0],s=3) # this line is equivalent to the subsequent one\n", + "plt.scatter(X_train.detach().numpy().squeeze(),time_deriv[0].detach().numpy().squeeze(),s=25, label = 'udot from DeepMoD autodiff', marker='x')\n", + "\n", + "#plt.scatter(X_train.detach().numpy().squeeze(),solution_derivatives[:,1],s=3) # this line is equivalent to the subsequent one\n", + "plt.scatter(X_train.detach().numpy().squeeze(),time_deriv[1].detach().numpy().squeeze(),s=25, label = 'vdot from DeepMoD autodiff', marker='x')\n", + "\n", + "\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -250,7 +357,7 @@ "array([40.59793231, 23.27768847])" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -285,7 +392,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -358,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/examples/PDE_2D_Advection-Diffusion.ipynb b/examples/PDE_2D_Advection-Diffusion.ipynb index 0d717e9..5970269 100644 --- a/examples/PDE_2D_Advection-Diffusion.ipynb +++ b/examples/PDE_2D_Advection-Diffusion.ipynb @@ -16,24 +16,17 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# General imports\n", "import numpy as np\n", "import torch\n", "import matplotlib.pylab as plt\n", "# DeepMoD stuff\n", + "import sys\n", + "sys.path.append('../src/')\n", "from deepymod_torch.DeepMod import DeepMod\n", "from deepymod_torch.library_functions import library_2Din_1Dout\n", "from deepymod_torch.training import train_deepmod, train_mse\n", @@ -63,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -86,19 +79,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -132,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -263,29 +254,67 @@ "output_type": "stream", "text": [ "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 25000 100.00% 0s 2.60e-05 2.09e-06 5.13e-06 1.88e-05 \n", + " 25000 100.00% 0s 2.83e-05 4.39e-06 4.38e-06 1.95e-05 \n", "[Parameter containing:\n", "tensor([[0.2501],\n", + " [0.4932],\n", " [0.4927],\n", - " [0.4943],\n", - " [0.4837]], requires_grad=True)]\n", + " [0.4852]], requires_grad=True)]\n", "[tensor([1, 2, 3, 4])]\n", "\n", "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 25000 100.00% 0s 1.36e-05 2.09e-06 1.15e-05 0.00e+00 " + " 25000 100.00% 0s 1.60e-05 5.20e-06 1.08e-05 0.00e+00 " ] } ], "source": [ + "%%time\n", "train_deepmod(model, X_train, y_train, optimizer, 25000, {'l1': 1e-5})" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "[tensor([1, 2, 3, 4])]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit.sparsity_mask" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Parameter containing:\n", + "tensor([[0.2501],\n", + " [0.4932],\n", + " [0.4927],\n", + " [0.4852]], requires_grad=True)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit.coeff_vector[0]" + ] } ], "metadata": { @@ -304,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/examples/PDE_Burgers.ipynb b/examples/PDE_Burgers.ipynb index 2a64cf8..5528454 100644 --- a/examples/PDE_Burgers.ipynb +++ b/examples/PDE_Burgers.ipynb @@ -18,24 +18,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# General imports\n", "import numpy as np\n", "import torch\n", "import matplotlib.pylab as plt\n", "# DeepMoD stuff\n", + "import sys\n", + "sys.path.append('../src/')\n", "from deepymod_torch.DeepMod import DeepMod\n", "from deepymod_torch.library_functions import library_1D_in\n", "from deepymod_torch.training import train_deepmod, train_mse\n", @@ -49,6 +42,36 @@ "%autoreload 2" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import deepymod_torch as dymod" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib as imp\n", + "imp.reload(dymod)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -58,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -83,19 +106,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -111,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -137,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -154,20 +175,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "number_of_samples = 1000\n", "\n", "idx = np.random.permutation(y.shape[0])\n", + "# DeepMoD mast be able to differentiate with respect to X_train, so\n", "X_train = torch.tensor(X[idx, :][:number_of_samples], dtype=torch.float32, requires_grad=True)\n", "y_train = torch.tensor(y_noisy[idx, :][:number_of_samples], dtype=torch.float32)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -191,19 +213,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -220,9 +240,9 @@ "axes[1].set_title('Noisy')\n", "\n", "sampled = np.array([y_noisy[index, 0] if index in idx[:number_of_samples] else np.nan for index in np.arange(data['x'].size)])\n", - "sampled = np.rot90(sampled.reshape(data['x'].shape)) #array needs to be rotated because of imshow\n", + "sampled = (sampled.reshape(data['x'].shape)) #array needs to be rotated because of imshow\n", "\n", - "im2 = axes[2].imshow(sampled, aspect='auto', cmap='coolwarm')\n", + "im2 = axes[2].pcolormesh(data['x'], data['t'],sampled, cmap='coolwarm')\n", "axes[2].set_xlabel('x')\n", "axes[2].set_title('Sampled')\n", "\n", @@ -247,12 +267,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "## Running DeepMoD\n", - "config = {'n_in': 2, 'hidden_dims': [20, 20, 20, 20, 20, 20], 'n_out': 1, 'library_function': library_1D_in, 'library_args':{'poly_order': 1, 'diff_order': 2}}" + "config = {'n_in': 2, 'hidden_dims': [20, 20, 20, 20, 20, 20], 'n_out': 1, 'library_function': library_1D_in, 'library_args':{'poly_order': 2, 'diff_order': 3}}" ] }, { @@ -264,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -272,6 +292,46 @@ "optimizer = torch.optim.Adam([{'params': model.network_parameters(), 'lr':0.001}, {'params': model.coeff_vector(), 'lr':0.005}])" ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DeepMod(\n", + " (network): Sequential(\n", + " (0): Linear(in_features=2, out_features=20, bias=True)\n", + " (1): Tanh()\n", + " (2): Linear(in_features=20, out_features=20, bias=True)\n", + " (3): Tanh()\n", + " (4): Linear(in_features=20, out_features=20, bias=True)\n", + " (5): Tanh()\n", + " (6): Linear(in_features=20, out_features=20, bias=True)\n", + " (7): Tanh()\n", + " (8): Linear(in_features=20, out_features=20, bias=True)\n", + " (9): Tanh()\n", + " (10): Linear(in_features=20, out_features=20, bias=True)\n", + " (11): Tanh()\n", + " (12): Linear(in_features=20, out_features=1, bias=True)\n", + " )\n", + " (library): Library()\n", + " (fit): Fitting(\n", + " (coeff_vector): ParameterList( (0): Parameter containing: [torch.float32 of size 12x1])\n", + " )\n", + ")" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -288,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -296,26 +356,20 @@ "output_type": "stream", "text": [ "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 100 0.40% 371s 2.29e-02 1.64e-02 6.41e-03 1.25e-04 " - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_deepmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m25000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'l1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1e-5\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Documents/GitHub/New_DeepMod_Simple/DeePyMoD_torch/src/deepymod_torch/training.py\u001b[0m in \u001b[0;36mtrain_deepmod\u001b[0;34m(model, data, target, optimizer, max_iterations, loss_func_args)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;34m'''Performs full deepmod cycle: trains model, thresholds and trains again for unbiased estimate. Updates model in-place.'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Train first cycle and get prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iterations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_func_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_deriv_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse_theta_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoeff_vector_list\u001b[0m \u001b[0;34m=\u001b[0m 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"source": [ + "%%time\n", "train_deepmod(model, X_train, y_train, optimizer, 25000, {'l1': 1e-5})" ] }, @@ -328,14 +382,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[tensor([2, 4])]\n" + "[tensor([2, 5])]\n" ] } ], @@ -345,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -353,14 +407,571 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - "tensor([[ 0.0974],\n", - " [-0.9905]], requires_grad=True)\n" + "tensor([[ 0.0973],\n", + " [-0.9869]], requires_grad=True)\n" ] } ], "source": [ "print(model.fit.coeff_vector[0])" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take the model and predict the time derivative and the theta vector using `library.functions.library_1D_in`:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "time_deriv, theta = model.library((model.network(X_train),X_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.pcolormesh(theta.detach().numpy())\n", + "plt.colorbar()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[tensor(2), tensor(5)]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(model.fit.sparsity_mask[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1.6510e-04, 1.0525e-08], grad_fn=)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "theta[3,list(model.fit.sparsity_mask[0])]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "sparse_theta, coeff_vector = model.fit(theta)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1.6510e-04, 1.0525e-08], grad_fn=)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sparse_theta[0][3]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([1000, 2]), torch.Size([2, 1]))" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sparse_theta[0].shape, coeff_vector[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.09734173],\n", + " [-0.9868601 ]], dtype=float32)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(coeff_vector[0].detach().numpy())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 3.6633506e-01, -2.4872100e-02],\n", + " [ 1.5475700e-03, 1.8591639e-06],\n", + " [ 1.8904690e-02, -1.9448838e-05],\n", + " ...,\n", + " [ 1.8463861e-02, 2.0664118e-02],\n", + " [-9.1108996e-03, 2.5876170e-02],\n", + " [ 1.8168192e-05, -5.3798169e-07]], dtype=float32)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sparse_theta[0].detach().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([1000, 2]),\n", + " Parameter containing:\n", + " tensor([[ 0.0973],\n", + " [-0.9869]], requires_grad=True))" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sparse_theta[0].shape, coeff_vector[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " {'poly_order': 2, 'diff_order': 3})" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config['library_function'], config['library_args']" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1000, 1])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.network(X_train).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.autograd import grad\n", + "prediction = model.network(X_train)\n", + "dy = grad(prediction, X_train, grad_outputs=torch.ones_like(prediction), create_graph=True)[0] # Calculate first order derivatives of prediction with respect to data" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([1000, 1]), torch.Size([1000, 3]))" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def library_deriv(data, prediction, max_order):\n", + " \"\"\"\n", + " Computes the time derivative and up to max_order spatial derivatives of the prediction tensor with respect to the data tensor.\n", + " \n", + " Args:\n", + " data (torch.Tensor): Input tensor of shape (batch_size, input_dim). Example: X_train.\n", + " prediction (torch.Tensor): Output tensor of shape (batch_size, output_dim). Example: y_train_pred.\n", + " max_order (int): Maximum order of spatial derivatives to compute.\n", + " \n", + " Returns:\n", + " time_deriv (torch.Tensor): Time derivative of the prediction tensor with respect to the data tensor.\n", + " du (torch.Tensor): Tensor of shape (batch_size, max_order+1) containing the computed spatial derivatives.\n", + " \"\"\"\n", + " \n", + " dy = grad(prediction, data, grad_outputs=torch.ones_like(prediction), create_graph=True)[0] # Calculate first order derivatives of prediction with respect to data\n", + " time_deriv = dy[:, 0:1] # First column is time derivative\n", + " \n", + " if max_order == 0: # If we only want the time derivative, du is just a scalar\n", + " du = torch.ones_like(time_deriv)\n", + " else: # Else we calculate the spatial derivatives\n", + " du = torch.cat((torch.ones_like(time_deriv), dy[:, 1:2]), dim=1) # second column of dy gives first order derivative\n", + " if max_order > 1: # If we want higher order derivatives, we calculate them successively and concatenate them to du\n", + " for order in np.arange(1, max_order):\n", + " du = torch.cat((du, grad(du[:, order:order+1], data, grad_outputs=torch.ones_like(prediction), create_graph=True)[0][:, 1:2]), dim=1)\n", + " \n", + " return time_deriv, du\n", + "\n", + "def library_poly(prediction, max_order):\n", + " # Calculate the polynomials of u\n", + " u = torch.ones_like(prediction)\n", + " for order in np.arange(1, max_order+1):\n", + " u = torch.cat((u, u[:, order-1:order] * prediction), dim=1)\n", + "\n", + " return u\n", + "\n", + "time_deriv, du = library_deriv(X_train, prediction, 2)\n", + "time_deriv.shape, du.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "poly_list = []\n", + "deriv_list = []\n", + "time_deriv_list = []\n", + "\n", + "# Creating lists for all outputs \n", + "for output in torch.arange(prediction.shape[1]): # loop over all dynamical variables modelled by PDE\n", + " time_deriv, du = library_deriv(X_train, prediction[:, output:output+1], 3)\n", + " u = library_poly(prediction[:, output:output+1], 2)\n", + "\n", + " poly_list.append(u)\n", + " deriv_list.append(du)\n", + " time_deriv_list.append(time_deriv)\n", + "\n", + "samples = time_deriv_list[0].shape[0] # number of samples\n", + "total_terms = poly_list[0].shape[1] * deriv_list[0].shape[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 3, 4)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples, poly_list[0].shape[1], deriv_list[0].shape[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([1000, 3, 1]), torch.Size([1000, 1, 4]))" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "poly_list[0][:, :, None].shape, deriv_list[0][:, None, :].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2, 3, 5])" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch1 = torch.rand(2, 3, 4)\n", + "torch2 = torch.rand(2, 4, 5)\n", + "torch.matmul(torch1, torch2).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([['', 'u_x', 'u_xx', 'u_xxx'],\n", + " ['u', 'uu_x', 'uu_xx', 'uu_xxx'],\n", + " ['u^2', 'u^2u_x', 'u^2u_xx', 'u^2u_xxx']], dtype=object)" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "string1 = np.array(['', 'u', 'u^2'], object)\n", + "string2 = np.array(['', 'u_x', 'u_xx','u_xxx'], object)\n", + "np.add.outer(np.array(['', 'u', 'u^2'], object),np.array(['', 'u_x', 'u_xx','u_xxx'], object))" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['', 'u_x', 'u_xx', 'u_xxx', 'u', 'uu_x', 'uu_xx', 'uu_xxx', 'u^2',\n", + " 'u^2u_x', 'u^2u_xx', 'u^2u_xxx'], dtype=object)" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "string1 = np.array(['', 'u', 'u^2'], object)\n", + "string2 = np.array(['', 'u_x', 'u_xx','u_xxx'], object)\n", + "np.add.outer(np.array(['', 'u', 'u^2'], object),np.array(['', 'u_x', 'u_xx','u_xxx'], object)).reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from functools import reduce\n", + "reduce" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "reduce" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 5)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a1 = np.random.rand(4)\n", + "a2 = np.random.rand(5)\n", + "np.multiply.outer(a1,a2).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 4, 5)" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a1 = np.random.rand(2, 4)\n", + "a2 = np.random.rand(2, 5)\n", + "np.matmul(a1[:,:,None],a2[:,None,:]).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/net/nfs/ssd1/gmiloshe/DeePyMoD_torch/examples/PDE_Burgers.ipynb Cell 55\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdeepymod_torch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutilities\u001b[39;00m \u001b[39mimport\u001b[39;00m terms_definition, string_matmul\n\u001b[0;32m----> 2\u001b[0m terms_definition(poly_list,deriv_list)\n", + "File \u001b[0;32m/net/nfs/ssd1/gmiloshe/DeePyMoD_torch/examples/../src/deepymod_torch/utilities.py:14\u001b[0m, in \u001b[0;36mterms_definition\u001b[0;34m(poly_list, deriv_list)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[39m\u001b[39m\u001b[39m''' Calculates which terms are in the library.'''\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(poly_list) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m---> 14\u001b[0m theta \u001b[39m=\u001b[39m string_matmul(poly_list[\u001b[39m0\u001b[39;49m], deriv_list[\u001b[39m0\u001b[39;49m]) \u001b[39m# If we have a single output, we simply calculate and flatten matrix product between polynomials and derivatives to get library\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 16\u001b[0m theta_uv \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(chain\u001b[39m.\u001b[39mfrom_iterable([string_matmul(u, v) \u001b[39mfor\u001b[39;00m u, v \u001b[39min\u001b[39;00m combinations(poly_list, \u001b[39m2\u001b[39m)])) \u001b[39m# calculate all unique combinations between polynomials\u001b[39;00m\n", + "File \u001b[0;32m/net/nfs/ssd1/gmiloshe/DeePyMoD_torch/examples/../src/deepymod_torch/utilities.py:7\u001b[0m, in \u001b[0;36mstring_matmul\u001b[0;34m(list_1, list_2)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstring_matmul\u001b[39m(list_1, list_2):\n\u001b[1;32m 6\u001b[0m \u001b[39m \u001b[39m\u001b[39m''' Matrix multiplication with strings.'''\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m prod \u001b[39m=\u001b[39m [element[\u001b[39m0\u001b[39m] \u001b[39m+\u001b[39m element[\u001b[39m1\u001b[39m] \u001b[39mfor\u001b[39;00m element \u001b[39min\u001b[39;00m product(list_1, list_2)]\n\u001b[1;32m 8\u001b[0m \u001b[39mreturn\u001b[39;00m prod\n", + "File \u001b[0;32m/net/nfs/ssd1/gmiloshe/DeePyMoD_torch/examples/../src/deepymod_torch/utilities.py:7\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstring_matmul\u001b[39m(list_1, list_2):\n\u001b[1;32m 6\u001b[0m \u001b[39m \u001b[39m\u001b[39m''' Matrix multiplication with strings.'''\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m prod \u001b[39m=\u001b[39m [element[\u001b[39m0\u001b[39;49m] \u001b[39m+\u001b[39;49m element[\u001b[39m1\u001b[39;49m] \u001b[39mfor\u001b[39;00m element \u001b[39min\u001b[39;00m product(list_1, list_2)]\n\u001b[1;32m 8\u001b[0m \u001b[39mreturn\u001b[39;00m prod\n", + "\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 0" + ] + } + ], + "source": [ + "from deepymod_torch.utilities import terms_definition, string_matmul\n", + "terms_definition(poly_list,deriv_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ac', 'ad', 'bc', 'bd']" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "string_matmul(['a','b'],['c','d'])" + ] } ], "metadata": { @@ -379,7 +990,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/examples/PDE_KdV.ipynb b/examples/PDE_KdV.ipynb index dad04df..c999519 100644 --- a/examples/PDE_KdV.ipynb +++ b/examples/PDE_KdV.ipynb @@ -27,6 +27,8 @@ "import torch\n", "import matplotlib.pylab as plt\n", "# DeepMoD stuff\n", + "import sys\n", + "sys.path.append('../src/')\n", "from deepymod_torch.DeepMod import DeepMod\n", "from deepymod_torch.library_functions import library_1D_in\n", "from deepymod_torch.training import train_deepmod, train_mse\n", @@ -79,14 +81,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -132,7 +132,7 @@ "metadata": {}, "outputs": [], "source": [ - "noise_level = 0.05\n", + "noise_level = 0.10\n", "y_noisy = y + noise_level * np.std(y) * np.random.randn(y[:,0].size, 1)" ] }, @@ -149,7 +149,7 @@ "metadata": {}, "outputs": [], "source": [ - "number_of_samples = 1000\n", + "number_of_samples = 2000\n", "\n", "idx = np.random.permutation(y.shape[0])\n", "X_train = torch.tensor(X[idx, :][:number_of_samples], dtype=torch.float32, requires_grad=True)\n", @@ -165,7 +165,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([1000, 2]) torch.Size([1000, 1])\n" + "torch.Size([2000, 2]) torch.Size([2000, 1])\n" ] } ], @@ -187,14 +187,52 @@ "outputs": [ { "data": { - "image/png": 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\n", 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YtmwZoqOj69W3kTJkyBAMGDAAf/7zn7Fv3z5kZmbi008/xXvvvYf77rsvqB7NnXfeidmzZ+POO+9E7969sW7dOv9dDT169uyJkSNH4qWXXkJ5eTmuuOIKrFq1Crt371b1+s6dOyMxMRELFixA8+bN0bRpU2RlZSEtLU3xdXfeeSfuuusuDB8+HNdeey22b9+OTz75pN4FTc+ePdGgQQM8+eSTKC8vR2xsLH73u98hKSlJ92cmosj05z//GW+88QaKiorQrVs3AEBmZibGjh2LV155BWVlZejXrx82bdqE1157DUOHDsWAAQNk13fdddchOTkZV155Jdq2bYudO3fixRdfxA033FCvhtmYMWP8RTYff/xx8z4kEUWEP/7xj/jtt98wbNgwZGRkoLq6GuvXr8dbb72Fjh07Yvz48SgtLUVMTAyGDBmC//3f/0VFRQUWLlyIpKQkyQLu4YqLi8PKlSsxduxYZGVl4eOPP8aHH36IRx55BG3atJF93ezZs7F69WpkZWVh4sSJ6Nq1K44fP46tW7fi888/x/HjxwEAEydOxIsvvogxY8Zgy5YtaNeuHd544w00adLE8M9CRBaxZY4rili7d+8W7r77buGCCy4Q4uLihMaNGwsZGRnCXXfdJRQWFgYtO3bsWKFp06aS6zl58qRw//33CykpKUKjRo2ECy+8UHj66aeFurq6oOV+++03YcKECUJCQoLQvHlz4fe//71w5MgR2anCA6dmFIRzU+ju3bvX/9ipU6eEe+65R2jVqpXQtGlTYciQIcKBAwdUTRUuCILw3nvvCV27dhUaNmwYNG144HS8YrW1tcJDDz0ktG7dWmjSpImQk5Mj7N69u95U4YIgCAsXLhQ6deokNGjQIGgqyA4dOgg33HBDvXWLp/MlosgSOFW4mG8628DYVFNTI8yYMUNIS0sTGjVqJKSmpgpTpkwRTp8+HfRacWx5+eWXhWuuuUZo1aqVEBsbK3Tu3FmYPHmyUF5eXu99q6qqhBYtWggJCQnCqVOnjPuwRBSRPv74Y+GOO+4QMjIyhGbNmgkxMTHCBRdcIPzxj38USktL/cu9//77Qo8ePYS4uDihY8eOwpNPPiksWrSoXltQrk0FQMjNzQ16bO/evQIA4emnn/Y/5mvj7tmzR7juuuuEJk2aCG3bthWmTZsm1NbW1lunuH1ZWloq5ObmCqmpqUKjRo2E5ORkYeDAgcIrr7wStNz+/fuFG2+8UWjSpInQunVr4d577xVWrlzJqcKJXCpKEFiplIiIiJzjzJkzSElJwZAhQ/Dqq6/avTlERIYaN24c/vWvf0nOxEpEJIc1b4iIiMhRVqxYgaNHj2LMmDF2bwoRERGRI7DmDRERETnCxo0b8c033+Dxxx/HJZdcgn79+tm9SURERESOwJ43RERE5Ajz58/H3XffjaSkJLz++ut2bw4RERGRY9iavJk1axYuu+wyNG/eHElJSRg6dCiKioqCljl9+jRyc3PRqlUrNGvWDMOHD5ecajqQIAiYOnUq2rVrh8aNGyM7Oxs//vijmR+FiMhQjI8UiZYsWYIzZ85g8+bN6N69u92bQ0RkiiVLlrDeDZHF5s2bh44dOyIuLg5ZWVnYtGmT7LJLlixBVFRU0F9cXFy95Xbu3Ikbb7wRCQkJaNq0KS677DIUFxeb9hlsTd6sXbsWubm52LBhAz777DPU1NTguuuuQ2VlpX+Z+++/H//5z3/w9ttvY+3atTh06BBuvvlmxfU+9dRT+Nvf/oYFCxZg48aNaNq0KXJycnD69GmzPxIRkSEYH4mI7KGlgU9ERM731ltvIT8/H9OmTcPWrVuRmZmJnJwcHDlyRPY18fHxOHz4sP9v//79Qc/v2bMHV111FTIyMrBmzRp88803eOyxxySTPIaxd7KrYL4pnNeuXSsIgiCUlZUJjRo1Et5++23/Mjt37hQACAUFBZLrqKurE5KTk4Om4ysrKxNiY2OFf/zjH+Z+ACIikzA+EhGZb/ny5UJMTIywaNEi4bvvvhMmTpwoJCYmBk0nTURE7tKnTx8hNzfX/+/a2lohJSVFmDVrluTyixcvFhISEhTXOWLECGH06NFGbmZIjipYXF5eDgBo2bIlAGDLli2oqalBdna2f5mMjAy0b98eBQUFuPzyy+utY+/evSgpKQl6TUJCArKyslBQUIDbbrut3muqqqpQVVXl/3ddXR2OHz+OVq1aISoqyrDPR0SRQxAEnDx5EikpKYiODr+TI+MjEXmF0fHRSHPmzMHEiRMxfvx4AMCCBQvw4YcfYtGiRXj44YeDlmV8JCKjOTk+Gu306dOorq7W/XpBEOrF2tjYWMTGxgY9Vl1djS1btmDKlCn+x6Kjo5GdnY2CggLZ9VdUVKBDhw6oq6vDpZdeir/+9a/o1q0bgLPx/sMPP8SDDz6InJwcbNu2DWlpaZgyZQqGDh2q+zOF4pjkTV1dHe677z5ceeWV/nHuJSUliImJQWJiYtCybdu2RUlJieR6fI+3bdtW9WtmzZqFGTNmhPkJiIjqO3DgAM4///yw1sH4SEReZER8NJLWBj7jIxGZxWnx0WinT5/G+fHx+KWmRvc6mjVrVq921LRp0zB9+vSgx44dO4ba2lrJ9u+uXbsk152eno5FixahR48eKC8vxzPPPIMrrrgC3333Hc4//3wcOXIEFRUVmD17Np544gk8+eSTWLlyJW6++WasXr3atNkyHZO8yc3NxY4dO/DVV19Z/t5TpkxBfn6+/9/l5eVo37497n5yN2Ljmhv+fkXbD4RcJj0zVfH1vufF61J6ndbtkXsPve/jRF0vOpeZvbBNmf//O9b8AABosrcQlYXf+B/ft3ZnvXV07NfF//9Ne/YAAPyW1hP7Gl3kf/zHo4lBr/n+hyo4UeD+CBS4bwL59pNY4GcXE+8LH6fuE72qTp/E/IcuQPPm4ccQN8bHwDilVTivVbuOwLhmRTxT2h7ftgTGXCfEWHE88MWBwPgIwB8jtcRH4FyckIoJTowHWuKjXGz00RMjAWfsFyO+n0bGRyNpbeDLxccDBw4gPj7e9O0lIu85ceIEUlNTHRcfjVZdXY1famrwn9490bRBA82vr6ytxZDNhfXirbjXjV59+/ZF3759/f++4oor0KVLF7z88st4/PHHUVdXBwC46aabcP/99wMAevbsifXr12PBggXeTt7k5eXhgw8+wLp164IyjMnJyaiurkZZWVnQ3eXS0lIkJydLrsv3eGlpKdq1axf0mp49e0q+Rqp7FQDExjVHbGPjf3wbxTQLucxPO39Fl0s7SD7X4/JuQevqcmkH7Nx6toCSnu2V256fdv4a9Hzg9uzcul92+9yie0bwMW/W7OxJmFazE4htiqZ7tgJNGiM6NubcMg3rnzLN/+/5Zr0uAQBUdr4UzQE0a3TuWDT+7dz/79hVhdjGxgQWI4n3RyDfvhFrXtNUevlG8t/DwH0RKLax/RclZgi367xb42NgnJKiFENCvVaNRjHNwtq+ULTEwJ1b9yu+n/g5NdtmRQxu3FQ6RjavORcfK7ZsOxcDFeIjADRr0hgA0KBZU+xt1AW+Xx5xTHBijJSLjxlJZQDqf8/kYqOPXIzcdSQRjRVe6oQ4Gfj99LU99H4X3T60SC4+xsfHM3lDRGFxe3xUq2mDBpLtB7XUxNvWrVujQYMG9WZkVWozizVq1AiXXHIJdu/e7V9nw4YN0bVr16DlunTpYurNVlsH0gmCgLy8PLz77rv44osvkJaWFvR8r1690KhRI6xatcr/WFFREYqLi4MyYYHS0tKQnJwc9JoTJ05g48aNsq+xmpYGv9p1GdWIV7uewIRRpNj7xXf1Hkv7XeiLrF1HEk3YGuucvTghq3kpPu7cur9evDA78WDW+qUuVkPFQjO2Rcv76yFOVvjiQFpN/d41agQmtwO5PT7qpXc/Ok2XSzu4/kZOICMa+ERE5CwxMTHo1atXUPu3rq4Oq1atUt3+ra2txbfffuu/+RkTE4PLLrsMRUVFQcv98MMP6NDBvN9FW5M3ubm5WLp0KZYtW4bmzZujpKQEJSUlOHXqFICzhTQnTJiA/Px8rF69Glu2bMH48ePRt2/foGKcGRkZePfddwGczVLed999eOKJJ/D+++/j22+/xZgxY5CSkmJq8SCtzG7saGnMq00AWX3xZSUmKKwRqRdqergpPmpJNOtdv1QCyChK6zUr7mn5LHYkhwKpiY9SyW09duyyv2dJuEIlZvY26qL4vByj943e80nL+eI2RjTwiYjIefLz87Fw4UK89tpr2LlzJ+6++25UVlb6i9OPGTMmqN7ZzJkz8emnn+Knn37C1q1bMXr0aOzfvx933nmnf5nJkyfjrbfewsKFC7F79268+OKL+M9//oM//OEPpn0OW4dNzZ8/HwDQv3//oMcXL16McePGAQCee+45REdHY/jw4aiqqkJOTg5eeumloOWLior8M7EAwIMPPojKykpMmjQJZWVluOqqq7By5Upz51zXydfolmvwaOkaH25vGLc3urSQ6wLva3Q33bMVAFCxZZuq9YnvKss1zr1wYWIG7pf6GB/PsmJ4ULjr98VeLesJjP2hXue0RLkvPoaipmeikykNJQXkk1p7G3VRTODo6XVjRozU+73S+jq3DbPOz8/H2LFj0bt3b/Tp0wdz584NauATEZH7jBgxAkePHsXUqVNRUlKCnj17YuXKlf4aZ8XFxUGze/3666+YOHEiSkpK0KJFC/Tq1Qvr168PGiY1bNgwLFiwALNmzcI999yD9PR0/Pvf/8ZVV11l2uewNXkjCELIZeLi4jBv3jzMmzdP9XqioqIwc+ZMzJw5M+xtdCM9SRy1r3FbI8xuXu5p4pVu/07lpfgYTsywM96oiXe+ZSIhLorPebXJbZ9QyW2S5vbkttS5oWbiBruEauATEZE75eXlIS8vT/K5NWvWBP37ueeew3PPPRdynXfccQfuuOMOIzZPFW9PHu9wvoRJqKSJGUOglF4bafQOCXD7XWUio4TqQWg1o7ZDTUw0Im6aOSRMj1A9TsRCxUdfz8RQnJak2LGrKuhPTCk5r5Sg2tuoi+TzcuvTejzsJD4fnPS91iIvLw/79+9HVVUVNm7ciKysLLs3iYiIiMkbtaQaIFY1uPUUpvQtp6cHjtJdZC8keDhkyly8qx65nBIfnLIdajil1073jFhNSQIjet0Y3TPRzN9jrQkUI3sm7thV5aoETiC573a4U40TERFFIiZvVJBLhDil0S0lcBiUniSTUg0e8h63XhgQqWFE3DK7nphTf0vEPRPV1rvxUdvrxghm7EOtSS0zGPn+dp8LREREpJ+tNW/cQpwI8T1m1LoD1ytFb0MpnKlklWrgBD7u1AsOI0XqFOEAZ+EiYwXWkLGqfpZR72Nn3R4r9pWvZ2BgoiDw/De6xpXb46PVsdGonpt2nwtERESkH3veaBBOMkTLus2iJ4HjdUxOWMftF2vkPmYmPZzW+8Cu7TGqHpjbhpUqxbNIK+butJpNREREXsXkjUpSFwBua6xEQjImFNa7MYbR9RzI+8SJFDXxyOkXhVZtm1WFk+UYNcuUGxkdn/TUBHNKzRunn49ERERex+RNmPQ2ZIwYCmXE69xci8EKkTxkikgrqYu7UENNlS4Iw60rZmbs8m2bkbMFhsvI95LqlRhuvZtISW7rEeo3wwn7SGkig0huJxAREVmFNW80kOqCL77wUNuAkVpObf0boxtJVtWecBoOmSIyllJc0/IaN/ElcJwwQ1+47xXYu2PXkURdMdJryW2je7yk1ex09Yx8bj9fiYiI3Iw9b1RSSqiE05jxJX+0JICsvJMrNczBrY03Dpkyn5svSoj0cmtMVOJL3CgNkZTqmUjuwmFQRERE7sHkjUbi6beNTGioSeCo6Z2jhZbtjtQeOlq56a4ywGnCyTyRXiNDzWd3y/4RJ7fVJm5CJbe9xuhixWYn/9VOxKDne+qW7zYREZFbMHmjUuCY7sAGiTjhomdGJ3HjSaonjtSYcr0XRnLbr2U73U7NcACjZlFxKyOHlbktoUXhC1XrRuk1djDjve0uNKyFEUncwPgornfjI44Fbu2Z6MVht0Z/F82coZOIiCgSMXljACMaJVrqJRiRSAnsOSSVFArFa8kcH72zqHDIlD7cP94gFTOsig9Ouig0c1vMXLc4cePFxITZvDg9uNbvnJPORSIiIi9i8kYDKxomcr1rpHrZBPYE0rJt4dbocSO19W7UkrurHAm8eJFC4bEzkat2yvFwng93W8xM8JtJrh4Yh0zp6024t1EX2X3hlISZXHvCTb3IiIiIvIrJG4P5hk7paazrafjIDefS+l5qtzlSGmecIpwoNKOTuWYlP9TMeGX2tOLhclPi3CvJbaWhZOJky95GXUImtuWed9LvRuC5ECm/90RERG7B5I0GcrVnAOsa1kpJFq0NLbkaPl5vsDnlDieR2xkxDbje2OmmZIYRrIrLeuOjnuQ2h02elZFU5qgEjhm83q4gIiKyApM3BlKqI6Pl9YD+RIweemreuAmnCJdn5ExTXhwaQeExuzeflljltCSRU+KsWbPN+eKjHDfERyO3USk+yiVu3LCPiIiIyDpM3jiIlq7KZg9XcMqFhVNwyBSRdkYVVjdj3WrpmUVQ7XqdTEs9MKVZptyc1DUysZVWs9NR+4K/8URERO7D5I0J9Na8AeydVlc8LEyuULKb6Z0iPNJwmnDSQ0+sCPUapZioJV4a2TtRLzfHUqWeiWpippfigFx8dFJyJhSzb9i4+btORETkVEzehMnIu6dSM0vZdXfW6XeF1eKQKefiPvI2I2dYcnI8MmqmP7n1iGuSGcmoKcLV9Ez0urSana6eiY8JSiIiIudj8kYnM2YnsbMQshSpXjgkzUt3lZW4+eKErKU2PjoxMaM21vmWM3MqcCfsn8DktlIvG6/MMqWHm3rdGIntAiIiIusweaOSUgPFrOltQ71f4LTkZidXzJ5K1wp6h0zxrjKROnbWzrLrItLK2GjV+8jVu1E7pJQ9E+uT2xdOTfyrPZ/E30lfu8Tt7QUiIiInYvJGQdH2A2EnRbS+VkuxYvG69TaWIr1XjdohU2K8MDknUu86k3PYcbEY+J5ui6FyQ6b09q5T2+vGLfFRa7Fi9ko8R825WLT9gAVbQkRE5C1M3qigZuhQqFoGRtVFsJLddXfCpbbejVriejduZ9YUwURupyfmuTVOylEbH0P1TNx1JNGxvUuczO4kl1nfZ197KD0z1ZT1ExEReRmTNwrSM1MlC2bKJWPE3efD6U5v5YWAk2stWEnvkCmvXZgYOdMURRZxbDTzAlDPc3rXGc56zVpnuNsTmBxQOud9PRONGjLlBt0zYmWTJ1bFR6ck193Wo4yIiMjLmLxRQS5Zo7UOjp4eOFYV/VSTnHIzJiTs4bXEFjmDUdOH63mdODaqiZVOi6fdM2INSQ54uR6Y0ckTtw6rMmNihki7MURERGQUJm90EM/CJPW8myl9NrfgFOHOxv3kTUbHPifFILlpy9V8ZjMKOVvxO6M3PsrheU9ERESkH5M3BlDTEPclfHwzMRi5bi3LqeWFBI4VIqlniVvvHJN59CSvzRrW5AZyn8Epn01vPTAxNw+ZMsPeRl007xOvJ7pYsJiIiEg7W5M369atw5AhQ5CSkoKoqCisWLEi6PmoqCjJv6efflp2ndOnT6+3fEZGRljbKXXXNJwCxFoTI4GJH9KOU4STG7khPupJ1Mi9JtSyXoh/4t47cr15zGRlLRW3Jre9njgxQjjDq1mwmIiI7DBv3jx07NgRcXFxyMrKwqZNm1S9bvny5YiKisLQoUODHq+oqEBeXh7OP/98NG7cGF27dsWCBQtM2PJzbE3eVFZWIjMzE/PmzZN8/vDhw0F/ixYtQlRUFIYPH6643m7dugW97quvvgp7W+0ovGnm+6qhp6eQE3DIlLV4l90cboqPRtAT67TGJrNimdOKGWuhZopwNcWKvTILnxM48bdE/H104w2lWbNm4bLLLkPz5s2RlJSEoUOHoqioKGiZ/v3710tw33XXXTZtMRERGeWtt95Cfn4+pk2bhq1btyIzMxM5OTk4cuSI4uv27duHP/3pT7j66qvrPZefn4+VK1di6dKl2LlzJ+677z7k5eXh/fffN+tjoKFpa1Zh8ODBGDx4sOzzycnJQf9+7733MGDAAHTq1ElxvQ0bNqz3WiMo9ZjZuXV/yOnC9dRKcAK3bKfV3HpXGXDOTCYkz23xUYqRsUNPDPXFarN7uCj1JnLDjIOB1Ca35Xom+pK54vjoxISEVdJqdtZLcu86kuiqQvpGfh+7XNoBVadOGLY+tdauXYvc3FxcdtllOHPmDB555BFcd911+P7779G0aVP/chMnTsTMmTP9/27SpInl20pERMaaM2cOJk6ciPHjxwMAFixYgA8//BCLFi3Cww8/LPma2tpajBo1CjNmzMCXX36JsrKyoOfXr1+PsWPHon///gCASZMm4eWXX8amTZtw4403mvI5XFPzprS0FB9++CEmTJgQctkff/wRKSkp6NSpE0aNGoXi4mLF5auqqnDixImgPzl674YaVbBS6W6Xrxuz23rK2I1Dps4x8mLCzcktt3FDfAw3Lum5eLS7d0AkJL59PRO9wqiZuAJJ9U50WuImEtoOK1euxLhx49CtWzdkZmZiyZIlKC4uxpYtW4KWa9KkCZKTk/1/8fHxNm0xERGFIm6jVlXVv1lUXV2NLVu2IDs72/9YdHQ0srOzUVBQILvumTNnIikpSbZ9fcUVV+D999/Hzz//DEEQsHr1avzwww+47rrrwv9gMmzteaPFa6+9hubNm+Pmm29WXC4rKwtLlixBeno6Dh8+jBkzZuDqq6/Gjh070Lx5c8nXzJo1CzNmzAh7G8V3eZUEDkeKhAa+XZzWQCYyg1Pio1SSWk2vl3B6qKgVKt5asQ1OY9SQqUih9fck1JBSuQS3L3lkZW8l8RBpo84HJ7dzysvLAQAtW7YMevzNN9/E0qVLkZycjCFDhuCxxx6T7X1TVVUVdKGglNwmIqL6OvbrguaxMZpfd7KqGti4BampwTXUpk2bhunTpwc9duzYMdTW1qJt27ZBj7dt2xa7du2SXP9XX32FV199FYWFhbLb8MILL2DSpEk4//zz0bBhQ0RHR2PhwoW45pprNH8etVzT82bRokUYNWoU4uLiFJcbPHgwbr31VvTo0QM5OTn46KOPUFZWhn/+85+yr5kyZQrKy8v9fwcOqJ8FQerOrpZCxFqW17J+r99Bk8N6N+YwcqYpr+8rO9gVH8VxRqouhtLzgcuZXTcmVC8cKy4uw/mMvteGs45QPUrUzjIVqT0TpaTV7JSNj77npJ53Ys9EMwqE2937TU5dXR3uu+8+XHnllejevbv/8f/5n//B0qVLsXr1akyZMgVvvPEGRo8eLbueWbNmISEhwf8nvoggIiJzHThwIKidOmXKlLDXefLkSdx+++1YuHAhWrduLbvcCy+8gA0bNuD999/Hli1b8OyzzyI3Nxeff/552NsgxxU9b7788ksUFRXhrbfe0vzaxMREXHTRRdi9e7fsMrGxsYiNDd1NWss0r2rvWokTOFp77Rgt0u4+6x0y5cSGN0Umu+Jj0fYDaBTTTNP7+WJXuDFGa3y14r3UCGc9Vs5MZXRy20127KrSNGxqb6Muhia3nc4rbYTc3Fzs2LGjXsH2SZMm+f//4osvRrt27TBw4EDs2bMHnTt3rreeKVOmID8/3//vEydOMIFDRGSh+Pj4kMNbW7dujQYNGqC0tDTo8dLSUsk6kHv27MG+ffswZMgQ/2N1dXUAztaOLCoqQkpKCh555BG8++67uOGGGwAAPXr0QGFhIZ555pmgIVpGckXPm1dffRW9evVCZmam5tdWVFRgz549aNeuXVjbIHcH16gaD4HrU/M6NY0nPWPYvdAo8+GQKYoEdsVHqal+1fS0kYsxvsSOmrgVTpzSExND9TCiYG4uVmxGMXctSa0du6r8f06jJnHjhnMjLy8PH3zwAVavXo3zzz9fcdmsrCwAkE1wx8bG+i8c1FxAEBGR9WJiYtCrVy+sWrXK/1hdXR1WrVqFvn371ls+IyMD3377LQoLC/1/N954IwYMGIDCwkKkpqaipqYGNTU1iI4OTqc0aNDAn+gxg609byoqKoJ+EPfu3YvCwkK0bNkS7du3B3D2Lsbbb7+NZ599VnIdAwcOxLBhw5CXlwcA+NOf/oQhQ4agQ4cOOHToEKZNm4YGDRpg5MiRYW1rYANe7oJEasiA1h41PmoaSUoJJC8lYdTikCnreeFOu1O5KT7KxSutcciKuKW38LGWdTi5zoePmuS2lp6JXo+PvBmgjpO/84Ig4I9//CPeffddrFmzBmlpaSFf46t1EO4NQCIisld+fj7Gjh2L3r17o0+fPpg7dy4qKyv9s0+NGTMG5513HmbNmoW4uLigIbXA2d7qAPyPx8TEoF+/fpg8eTIaN26MDh06YO3atXj99dcxZ84c0z6HrcmbzZs3Y8CAAf5/+7qejh07FkuWLAEALF++HIIgyF5c7NmzB8eOHfP/++DBgxg5ciR++eUXtGnTBldddRU2bNiANm3ahL29SsOV5Go9BN5N1tqoEff20fracIcoeKV7tNHcPmSK04S7g9vio1M4IW7peX+7ttuo5LZXaB02FYqXEtxabzA5TW5uLpYtW4b33nsPzZs3R0lJCQAgISEBjRs3xp49e7Bs2TJcf/31aNWqFb755hvcf//9uOaaa9CjRw+bt56IiMIxYsQIHD16FFOnTkVJSQl69uyJlStX+osYFxcX1+tFE8ry5csxZcoUjBo1CsePH0eHDh3wl7/8BXfddZcZHwEAECUIgmDa2l3qxIkTSEhIwH3PlyK2cXAXWKUuwaF62oSTwAmnq7LbGlh6BTa4A++Syl2chLqrLNfzxs1DAgD55I3SnWW5mg5yFyZKCS637S+9qk6dwNx726K8vNxTXemV4qMabrzoA4xJiNtJfN6LZ5qSSt4oxUjGx7NC1btxS4xU+l6Lnwv1b6nXA8FtETviY1RUlOTjixcvxrhx43DgwAGMHj0aO3bsQGVlJVJTUzFs2DA8+uijqrfRFx+9FveJyDqREkd8n/OnB0frnm2q01NLPb+fxFxRsNiNpBoz4XTX19ILx8yCxm6n9q6yD4dMGYv7y/tCXchZkQAxY/1GFwy2Mgkkl7hRwiFTwXYdSfT00CktM7GJEzfhrNtKoe5VpqamYu3atRZtDRERkXauKFhM4dFTuNht1Na7Uct3V5mMnSacCNB/Mac2jukdtqSXntc65YIW4JApNaQSN2b0uiEiIiKSw+SNBnIzTsk9byTxnS5xQkZ8V1hq++gcThFOZBwzY18go9dt1Ha7NdbKDoeUiI+RwsjeQnL7V6kHj1t6K1l1zhMREdE5TN5oIJ4iXM004WqnvlX7/oHbIR4e5YUpPI3g5a7tRE6kp5aXnnhkdAyLhItOPUOmtAjsXeL25Hb3jFjJXpxy+yxUMWKtPW/MStyY+dsfCecQERGRUzB5YyKj7kyFWk+ohpk42eO1JA6nCFePs6iQE8j1DFQTy8hYauOjuFAxuYcZ5w1vFhEREVmPBYs1srpBEtiDR0uRQLnnefEjjUOmiJxL7ax7VnDrbFNi4dayiqR6N3roSW53z4h15U0BKV44R4iIiJyGPW900FNPRm+PFzUzSyn1qFGa+tOLd8b0zqIS6YwcSsEkF4mFG2vUxlwrYppSrbNI5uYpwo3skeijJznmpn1GRERE1mPyRiM7i1qK6+coDTtQm/RxOw6Zco9I2GeReiFv1HCncGOSUcNTpf5txnvqeT+z6S3mHshL53qk1lBzyveRiIiIzmHyxmXkLhSUethQ+CK1NwmnCSc1vJAIFn8GIz6Tlvhrxj6UK1YsTm6rpTa5bUZPFjPt2FUlu81Ksd/L8dEL5zQREZHXsOaNBYy8mxx4MSDuZWPGxYeb6B0ypfWuMhHZz+raM3rez8jtC/fzKsVHtT0T5URqcpvq80pNKCIiIidiz5swGDEMygxy9Wy8WucmFK0XJhwyFRpnmiIzGN1TxYh4p7VYstb3tHp4lpjaWaZ81BQqZozUPk24VzBxQ0REZB4mb8KkpnaM1mLCSutRWofvfQKLGHuZ2no3anl9Cly3DWVwo8Di4UXbD9i9OY4TKoGs58JPb5wzK2niW15u/VbF5VDxUYqWYu5eS267dbvt4PW2BRERkVNx2FSYrGzESF0UyPWw8UpBYiPpHTLl9TulkVqQ0yyRkDjVy+qYpPR+Zm+LmvpkVg0xkTrHjUpuR2p8VEyC6eiZ6Ku744YkEtsWRERE9mDPGwVF2w+onkVFbRd+Iy7q9CZmvJzQYQLCGbx+IUfGcHNyS8+2y73G6fE4UuqBae2V6OVCxURERORcTN6EoOYuupYEjpG8nIwJhVOEuwv3W2QxYupwu2vBKFGzbUrDpuxKXomTDoHxUcuQKTluPc+VZpuSEqpnTVrNTk0JHt9+s3r/GflddHNCloiIyC2YvLGY3MxRRovU4sRGi+SeJLy7rF+XSzsgPTPV7s2wVbjxx+ikjNL2KBV4Fz+nJ2keuLwTku5GTRHu9fhodI9Opf1lR00yI7+HoWo9ibEmGBERkXZM3igQX3ypSYhoaZjrbTiJt8P3nlLri4QEDqcIJ3IWu5MTUvQkXML5HE7ZB3oTEJEUH81InGipe+OlYvJqkjiR0C4hIiIyA5M3GjilMR44m5SvESQ1na1XZ53ikCl7cZpw0sqqGKT3fcwobGxX78fuGbGakwFqhkypmSLca+R6yoTqlag1Ru7YVWXb74tZ7Ro7i4UTERF5FZM3Ksg1NPRMeStOtqgRalkj6ktQfV4aEuClO7vkPlbFICsLxxuxLVYSJ7d9QiVuxLNMMXmrj9OGTBEREZH7MHmjkbhLcDgJHK3vqXUdXutxI4VDpsJjZE0HLyW79ApMDER6TQenJS98tA6HCoyjWpPvVu8DcRJA6fxW2zNRjvh8d2vPRKXECWcxNE+k1wQjIiLSg8kbnZTqzIiXk6P3DnDgEKnAv0hI1hA5mVMTFlbQ06tQ67rNIFesWIrTj69Rw2/Eye1IHDKlV6T0TDLzfCciIiJpTN5o5LQifOL39HpDivVurGHkTFORtu+cfoHvRmbuU6l1h5OUt5NcvRu957PaIVORdo4rJWgiZZY+3zng1HOBiIjIi5i8USHwDpPaIUxG1rQRkxq6Je6NQ2fpHTLFIUBE2oS6mLOyaLGRSW0j42m49cvCJU5uqylUbBcn3IjQW7DYzQkco/Y7e+YQEREZj8kbFXxDkuQa8YEXLeEOhSL1WI+AnIbnsTwrixaL38vuhLbUbIBS7N5OJ7FqXxjZa8jXI0eqZ45bfq+M2u9yyVzGSCIiIv2YvFFJ7d1kcYPFrIaK0vZ4tXHEIVP2i5R6DnqIzzsW5HQvI2Oo3LqMjtNqixWHio++noniIVOBAnukuDU+qpnhSWofGj1NOHB2Hzp1ximjv6esz0dERKQfkzcaqO2KH5i4UTuMKXAZp85kEim8NmTKqRcFXsKLEWcJ53iYcbGqp66OUcIdwhMque1mcjWCzBDqd8WpSTC931M9M3ESERGRMiZvNFDb4PD1ftF7h0lLwybUsl69qOQU4eHjNOHGESdfvXreuUU4F4dGX1g65fvg65noo7feTaSf62bwYoKdCRoiIiLjMXmjoGj7gbDXobUBY3ayx6u0Dpny4ZApc0XS/hOfh0bEj0hj1RCjUOszMqY6KT5rjY9epic26e3FpJTwsitGOiGhSERERNrYmrxZt24dhgwZgpSUFERFRWHFihVBz48bNw5RUVFBf4MGDQq53nnz5qFjx46Ii4tDVlYWNm3apGv70jNTFe+aBnaH9y0nV5hSqdix2QX9vNBIC1XvRiuleg6Rzs0zpdhFasa3cGveOD0+miFUUXiz38cIdvS0kat3I64HFiruievd+JLbXqWUODGyZ2KodUVCAscJPdCmT59eL2ZmZGT4nz99+jRyc3PRqlUrNGvWDMOHD0dpaamNW0xEREbS2wZevnw5oqKiMHTo0KDHBUHA1KlT0a5dOzRu3BjZ2dn48ccfTdjyc2xN3lRWViIzMxPz5s2TXWbQoEE4fPiw/+8f//iH4jrfeust5OfnY9q0adi6dSsyMzORk5ODI0eOaN4+351ztTVrpJI5vv8PXCZQqKSPUexuNFmNU4ST2zk9PqoVGHusnD5Y6j2seF+5GjdWxWClRIHenjderHdjBicnvqUmV7CyV5jV7yenW7duQTHzq6++8j93//334z//+Q/efvttrF27FocOHcLNN99s49YSEZFR9LaB9+3bhz/96U+4+uqr6z331FNP4W9/+xsWLFiAjRs3omnTpsjJycHp06fN+hj2Jm8GDx6MJ554AsOGDZNdJjY2FsnJyf6/Fi1aKK5zzpw5mDhxIsaPH4+uXbtiwYIFaNKkCRYtWmT05svSU3zYiPeMlPo3bplylSKD3HkV7rApr8THwLgkN32w2e+r9JgSI2OmEy5cfYyod7NjV1VEDYsE9Cex3HpTwCtthkANGzYMipmtW7cGAJSXl+PVV1/FnDlz8Lvf/Q69evXC4sWLsX79emzYsMHmrSYionDpaQPX1tZi1KhRmDFjBjp16hT0nCAImDt3Lh599FHcdNNN6NGjB15//XUcOnSoXm95Izm+5s2aNWuQlJSE9PR03H333fjll19kl62ursaWLVuQnZ3tfyw6OhrZ2dkoKCiQfV1VVRVOnDgR9KdET4NG7q5XqN46xCnCw2FkIUzefVcmdR6bzWnx0arPbfZU3mp7Qjp5ZkBxLxC9U4Sbdd5L9ciyktaZpszoVWPmFOFGxCG5762b2yc//vgjUlJS0KlTJ4waNQrFxcUAgC1btqCmpiYoPmZkZKB9+/aGth+JiMhY4hhcVVX/+k1vG3jmzJlISkrChAkT6j23d+9elJSUBK0zISEBWVlZiusMV0PT1myAQYMG4eabb0ZaWhr27NmDRx55BIMHD0ZBQQEaNGhQb/ljx46htrYWbdu2DXq8bdu22LVrl+z7zJo1CzNmzFC9XUoNGrlaN+KGlHjZwOWU3kMrvTNeuRmHTJEdrD7PnBgfrUpSeLWgsB6hLv7Fs0ypFareTfeM2LAS3FK/f04h1bsznCRWRlKZ7O+LmTcJzNrHapKagW0acW9ku453VlYWlixZgvT0dBw+fBgzZszA1VdfjR07dqCkpAQxMTFITEwMek3btm1RUlIiu06t7UciIgrWtGcPNGvSWPPr6n47BQBITQ2uMTlt2jRMnz496DE9beCvvvoKr776KgoLCyWf9/02SK1T6XcjXI5O3tx2223+/7/44ovRo0cPdO7cGWvWrMHAgQMNe58pU6YgPz/f/+8TJ04EfRECL8rUNjqUCm9K1cLRun6l95VKDHklgcMhU8bgNOHGCDyHxedYemYqPjbxvZ0SH70o3DjvRUae50oX8FZd3PuSJnb3vjGLnd9Nqdp+4iGTdszGN3jwYP//9+jRA1lZWejQoQP++c9/onFj7RcOQGTGRyIiJzlw4ADi4+P9/46NDb9H68mTJ3H77bdj4cKF/uG1TuHo5I1Yp06d0Lp1a+zevVvy4qR169Zo0KBBvdkBSktLkZycLLve2NhYyQOdnpmKn3b+Wq9XjBy1s0aJL/TMaKxKDdNyIw6Zcq9I2IdKscHqixOr46MWdt5tjyRqkrJaeiYq9TYxqteNlufslFazE3sbdVFM4Mjtr0hOcAPSbSOzk9tqJCYm4qKLLsLu3btx7bXXorq6GmVlZUG9b6yIj0REpF98fHxQ8kaK1jbwnj17sG/fPgwZMsT/WF1dHYCztdOKior8rystLUW7du2C1tmzZ0+9Hyckx9e8CXTw4EH88ssvQTsoUExMDHr16oVVq1b5H6urq8OqVavQt2/fsN9f6/hvpSLCZjdQ5Wa1ImmR3rj2cdOdZSdxwgWn1fGxaPsB2Zhi10xPUpwQ98zaBqOT2z5enyLcR0uvGy8VK3bCOeEEFRUV2LNnD9q1a4devXqhUaNGQfGxqKgIxcXFhrQfiYjIPlrbwBkZGfj2229RWFjo/7vxxhsxYMAAFBYWIjU1FWlpaUhOTg5a54kTJ7Bx40ZTfzdsTd5UVFT4dwhwtvBPYWEhiouLUVFRgcmTJ2PDhg3Yt28fVq1ahZtuugkXXHABcnJy/OsYOHAgXnzxRf+/8/PzsXDhQrz22mvYuXMn7r77blRWVmL8+PGat0/NnXMtFy9Kj4uXCSfZonQh6eZGm9F3lYmczOnxEdA2vMiIBJee+OWEeGhGcs+MQrfiYsVy3N6rTmuxYjO3wwuUziMjZn0zyp/+9CesXbsW+/btw/r16zFs2DA0aNAAI0eOREJCAiZMmID8/HysXr0aW7Zswfjx49G3b19cfvnltmwvEREZJ1QbeMyYMZgyZQoAIC4uDt27dw/6S0xMRPPmzdG9e3fExMQgKioK9913H5544gm8//77+PbbbzFmzBikpKRg6NChpn0OW4dNbd68GQMGDPD/2zdueOzYsZg/fz6++eYbvPbaaygrK0NKSgquu+46PP7440FdVPfs2YNjx475/z1ixAgcPXoUU6dORUlJCXr27ImVK1fWKyakhVKXfzWNkMBhUlrqzxhR/waoX5fDa0MY9N5V5pAp9TjTVGhG15ZyenxMz5Sv62BGPRMz4pZb46CvWHDgxX+4yW2lWaac2HskHHpmedLbK1GpWLFdIvU8OnjwIEaOHIlffvkFbdq0wVVXXYUNGzagTZs2AIDnnnsO0dHRGD58OKqqqpCTk4OXXnrJ5q0mIiIjhGoDFxcXIzpaW7+WBx98EJWVlZg0aRLKyspw1VVXYeXKlYiLizPjIwCwOXnTv39/CIIg+/wnn3wSch379u2r91heXh7y8vLC2bR61F44KA2hUsvoi0C3NKzE1A4JUEvtXWWv8Mpd3UjlpvhohVA9aNwa58yid5apULyQ3DYjNnohwa00S5QXLF++XPH5uLg4zJs3D/PmzbNoi4iIyEpKbeA1a9YovnbJkiX1HouKisLMmTMxc+ZMA7ZOHVfVvLGbmoSKeEpMtUmYcIdKaeG1BpkPpwhXhzN2kZHEscuN9bXcts1KvW7EPUTcUu/GTftfTKmQsdIU4U5LhKmdnCFcbj7WREREdmLyxgJqZn4KrAlhZsPGrYkbJhycKdISX6EYVdvFzewo1G7UEFMzWXHBqjgTksOT20b3OLWSniFVTqm7I0VrHNNaoDzSYyQREZFeTN5opLb3TahpwwOXYUOmPk4R7m7cj5HDF8tCxTyrmPW+4fTOMXqbQl30642PoZh9XgfWZTMT45Mx5Gr5SQ25cmtijoiIyEmYvNFBb8FhqaFRoeo4sMFjLvYcOYfThIcv8JxVKugbCexKSku9r9aeAUbULhO/zqj9oae3hlSvGyni5LY4PhrRU0Tr8GMzOKXmjRuSSGp70Mj9PxERERmHyRubyNW4kbuLHck4RTi5ReB5W7T9gI1bYj4nfz41Q1XF5C4+vThDH6AvPhqRbAi1H83cz2qSNnqG6GpN3Dix3o0co46H184fIiIiOzB5o5PaXjFqkjFq7mxFUg8cDplyFi/MomIl3/nu9Z436ZmpmuKSnp4uZvLKxaTR9cAi4XyXi/WsrUZEREROxuSNA6gp7umVCw0n8fKQKacWwiRv0RKXrIxh4p4z4a7LqG03IkmlNsmsdZYpo97XyQI/g9Tn0fObIJfsctrvi903gOx+fyIiIi9g8sZCchcAWqcTjyQcMmUsI+8sO+3ixE6ReG4ayczEjtp1W3H8jP6cUlOE+3om+oSqdyPumejj9fNbKsEtFx/11APLSCqT3Yd2JNetSp4yDhIREZmnod0bQGepKWLM3jfnGH1XmShc4iGONdUVdm6O69hdV8YN8dWoi349ye3uGbGe6H0DsGeiHmrPTzecR0RERG7Fnjdh0nLH3ejlvEhtvRu1WO9GHSNnmoq0fSm+qImUixdfwWKj4lWk7DczaImPUokbca8bMTcV2A2Hkb2NvNhzKZxzPRLr9xERERmNyRsLcRYp4+kdMuXFhjXZQ3xO+5I5kVCwGLAm6WLGsDQvX0RqLebuEwnFipWwYLEyPee6npnfiIiISBqTNwZTOwNVqOe9fGGhFhvS5Case2MuvRd9dh0Xs99Tbz0wNQKT217rcaN1yJSRvRJ93LZP9X6XGQ+JiIiMxeSNQbQ2UkJNHR6Jd6c4RbjzRPqdeC3EMxz5hhVFssC4aMSQC7Xv5QRGx/BQSQet8VGM53p9Xk/cqD1n1PQalloXexsTEREZi8kbAxmZdHHahYiXeH3IFItx2ocXKsH0XPBped6o7SBrePF3TWuCx0nx2cjzQpy8luPF7wAREZFVmLzRSKqxY9aFQSQ3cjhFuPE4Tbh1IqHmjVZaYqcv9mmJrVpn6Qv13m4hlzwIFR/lpgg3U6gep17npF43dmEilYiISD8mbzQST+kdWJ9Ga6NEb1dkLzJrSACHTFkrkvdnpJyrThfucXDixaU4PsolYvUOmQpkV2LW7v0utU9DDSXb26iL5DJy+9BL0637yA2XIiIiIuMxeWMQNlbMobfmgHgWFZJnRl2HSBJYEDdS61UZze59GHg8w3m92bRMES7Fyl43XuS12GllApo1wYiIiLRj8kYFuR4ycrOYaJ3dJFQPHM5iUx+nCCensDvR4DRK8cottTB8PSr1Hlsnfiekhkz5+HqPiOOj13qJkLxwvu9qCpOLH+ewUiIiIu0a2r0BbqDlooJ33sPHKcLJbQKHT/r+W1NdYecm2SZwX6ilJ3EjF2uNir9OjeNGxUepXjcZSWWeTXArDVmS2qfh9Krxrc9N+zKc73vga8Xr0TusnIiIiOpjzxsNfD1k7GqEOOnOtJE4RbhxjJzJhFMHa8OLk3P0FA/WUzOMpIdMSfVM9FEaUuqmZIMTuCVGmtV2ULNeThdORERkHCZvVFJTWNiIBpKaIVSRjkOmyA5qL1QijdUxSe37Gb2c04h7higlt5XioxUJCCfs4x27qmST21K/DeHsl11HEl35e6P1OCm1V+SGlPuw5g0REZF2TN6oJE7OKPXCUZvIES8jNwQgki4IOWTKHJwm3DqRds7KDZMI9ZhR72fVcnLMTkwEJhzUzDKl1OtGTClBYWTPRCecD0b2SvQCzhJFRETkPkze6GDUHd1wEj9ewSFT3uHlfRpJ56QZnH5RGE7cNfOzhYqPWonjYyArkrJOPY+Mrnkjx64YaWZtKL3vyYLFRERE2jF5o1FgUdLAQny+Ron4rrsRjVXxOiItwWME9haR5rWpbs3mO/dCnYORcH7KDXtwYqJGTSLdqO1WM/OO0ZTq2Khh1SxTdn43jP5McjcG+FsTLBJiIRERkVWYvAmTeIYZn8BhVVqnDZdaf+BzXh2WoWZoj956N0RG8p1/cud2l0s7eP7Osps+n5qaZUYQz4BldpxW0zOR8fEsrcOm9jbqojiszCuJbz1DvH2PybV/AnmxrUJERGQXJm8MoHZ4lNoLBKXGTiQ2hNQOmfLhkKnwuWUWFfIOO+/Qmz29uBGfTW9y2yfc3jnkTWq++0r1+MQJSyIiIjIPkzcK0jNTNTVKQg2X0tILR64ejpeorXejVqRfnLAgp3mUesR57by0S6j4aWTvGCOXU0PPxa3R9cDEzExuO/Gc0PK5fPtYqXeNngS3WTFa7yQJWt9DSqjZMZ34XSAiInIrW5M369atw5AhQ5CSkoKoqCisWLHC/1xNTQ0eeughXHzxxWjatClSUlIwZswYHDp0SHGd06dPR1RUVNBfRkZGWNupZ5iS3PJak0FqZnKJFJwiXB/O4GUMq4cruiU+mkEq7pndO0bvcoD5cdnIc1iqWLHR3N4TQ2/Pw1C/NXbXEgrnuOh5rVeHeBMRkTvNmzcPHTt2RFxcHLKysrBp0ybZZd955x307t0biYmJaNq0KXr27Ik33njD/7zetni4bE3eVFZWIjMzE/Pmzav33G+//YatW7fisccew9atW/HOO++gqKgIN954Y8j1duvWDYcPH/b/ffXVV2Zsfj1Kd5mMbNx7MYHDBIM7RHoiTI4Zd5i9Fh/DYdUFoJoaHk7F5LYyI3u96E3umNk70opzRKl944ZzpmPHjvWS11FRUcjNzQUA9O/fv95zd911l81bTURERnjrrbeQn5+PadOmYevWrcjMzEROTg6OHDkiuXzLli3x5z//GQUFBfjmm28wfvx4jB8/Hp988gmA8Nri4Who6tpDGDx4MAYPHiz5XEJCAj777LOgx1588UX06dMHxcXFaN++vex6GzZsiOTkZEO3NVxG3jUWz3TlRpwi3FsiZb/6eoAoXcQYdU66OT66tQ5GYFF4O4W6yNc6pNQnVHyMRHI3DpSGTKXV7NS8D70QI+0+L8L19ddfo7a21v/vHTt24Nprr8Wtt97qf2zixImYOXOm/99NmjSxdBuJiMgcc+bMwcSJEzF+/HgAwIIFC/Dhhx9i0aJFePjhh+st379//6B/33vvvXjttdfw1VdfIScnJ6y2eDhcVfOmvLwcUVFRSExMVFzuxx9/REpKCjp16oRRo0ahuLhYcfmqqiqcOHEi6E+KmV2Atc5I5fZGlBF4Vzk8XpktxQriGjdK56Bdd6Dtjo+BjIhPbriT72NVPBafs1qT26F4IcHgRE7br+JzK9xzzS3tkTZt2iA5Odn/98EHH6Bz587o16+ff5kmTZoELRMfH6+4Tj3xkYiIjCOOwVVV9X9zq6ursWXLFmRnZ/sfi46ORnZ2NgoKCkK+hyAIWLVqFYqKinDNNdfILqe2LR4OW3veaHH69Gk89NBDGDlypOKPaVZWFpYsWYL09HQcPnwYM2bMwNVXX40dO3agefPmkq+ZNWsWZsyYYdi2au0Vo3a2KiPey4nCnUWFKNI5OT66tRdOJIiU5DaLuSuz4/ws2n7A8vcMVF1djaVLlyI/Px9RUVH+x998800sXboUycnJGDJkCB577DHF3jdGtx+JiCLNb2k90aBZU+2vq6gEAKSmpgY9Pm3aNEyfPj3osWPHjqG2thZt27YNerxt27bYtWuX7HuUl5fjvPPOQ1VVFRo0aICXXnoJ1157reSyatvi4XJF8qampga///3vIQgC5s+fr7hs4DCDHj16ICsrCx06dMA///lPTJgwQfI1U6ZMQX5+vv/fJ06cqPdFMFOkXdhwyJSzcUhFfeKhUqESrju37kdNdYXZmwXA+fExVHwLJ7kT+Fo163FjIklvclvcM9GKQsVuo7Rv9zbqItk70QvxMdxzQOo80rrO9MxUfBzWVoRnxYoVKCsrw7hx4/yP/c///A86dOiAlJQUfPPNN3jooYdQVFSEd955R3Y9drcfiYgi3YEDB4KSJbGxxt2wad68OQoLC1FRUYFVq1YhPz8fnTp1qjekSktbPFyOT974dsb+/fvxxRdfaM5kJSYm4qKLLsLu3btll4mNjZU90FI9W7RM+a1F4HtJNY7EjwVetFB9kXJXGeCdZStoOe+7XNoBVadOmH5xYnd89AknKaJ1Zr7A95KKh25nVnLbiZycTJMbVipX7yaSfm+84NVXX8XgwYORkpLif2zSpEn+/7/44ovRrl07DBw4EHv27EHnzp0l16MmPhIRkXni4+NDtn9bt26NBg0aoLS0NOjx0tJSxTqQ0dHRuOCCCwAAPXv2xM6dOzFr1qyg5E24bXGtHF3zxrczfvzxR3z++edo1aqV5nVUVFRgz549aNeunebXKnXrVdPg1DMDQ6iCmVLrc2rjVw2j7iqTNCNn8eLFibp6U1adj3bHx0Bap9UON+Gs9/VOqFmmhRmz8FnZMzGcIcFG0vrZWA8sfE6+qbR//358/vnnuPPOOxWXy8rKAgDF5DYRETlfTEwMevXqhVWrVvkfq6urw6pVq9C3b1/V66mrqwuqqWNEW1wrW3veVFRUBP0o7t27F4WFhWjZsiXatWuHW265BVu3bsUHH3yA2tpalJSUADg7dVdMTAwAYODAgRg2bBjy8vIAAH/6058wZMgQdOjQAYcOHcK0adPQoEEDjBw5UvP2pWemIrZx/eyZVG8cIxoqUj1pvHhnWQ21d5XFOGSK7GZUbwKnx0dAPhYqfX4rp/226r3sjM1664FZkYx1ym+Wlp6JTNyEpua4yvUgdoLFixcjKSkJN9xwg+JyhYWFABB2cpuIiOyXn5+PsWPHonfv3ujTpw/mzp2LyspK/+xTY8aMwXnnnYdZs2YBOFvTrHfv3ujcuTOqqqrw0Ucf4Y033vAPi6qpqVHVFjearcmbzZs3Y8CAAf5/+8YNjx07FtOnT8f7778P4Gw3pUCrV6/2d1fas2cPjh075n/u4MGDGDlyJH755Re0adMGV111FTZs2IA2bdoYvv2hpg0WL0fqhwSoJa53Q+oYeYESCYkxrefwzq370alLi7De0w3xUSr+mVFPQ+p91WybEeS2x4y4btQU4XL1brxQr8VscrVuAp93GyvaIFrfw66CxXV1dVi8eDHGjh2Lhg3PNYH37NmDZcuW4frrr0erVq3wzTff4P7778c111yDHj162LKtRERknBEjRuDo0aOYOnUqSkpK0LNnT6xcudJfxLi4uBjR0ecGJVVWVuIPf/gDDh48iMaNGyMjIwNLly7FiBEjAAA///yzqra40WxN3vTv3x+CIMg+r/Scz759+4L+vXz58nA3y69o+wH0uFx5eI7aHjd6Gk/injiRmADSO2SKQ3zILGqTtuFenDg9PvqoqU0j9Zjc/zthWJodCXelxI2bpgjnzQrzad3Heo6HEcdRqUeeXQWLP//8cxQXF+OOO+4IejwmJgaff/65/05samoqhg8fjkcffdSGrSQiIjPk5eX5e6OLrVmzJujfTzzxBJ544gnZdXXs2FFVW9xoji9Y7FRqiwVrGU6gdd1uZ0Y9ByKzaJltiqTJDQN10sW+2hmsnLDNSsltpcSNmcltJ+wXsp4Tkq5qXHfddZKN7dTUVKxdu9aGLSIiIlLP0QWL3UBNAdNAWop1al2303GKcOdz45AAK3jpPLSKk/aZXNx1SgF4qdjoS26zBkt4vDgTnxXfUfFNJCasiYiI7MfkTQhqG/dqZocKdypdI2ZpiQSRNmTKixcnbuG1BKvVtMYzueV9sVHuebnjZNasgWbQmtz2EdcDE8dHJreDeTVZpvd7rHY4o5btICIiIn2YvFFBTaMnsC6NVJ2HwHWE0whSU2vDDThFuPk4TbhxQiUNSB89dcDkEupq16XlePnWq3XdWr8T4gSK3viouLxFvep4PjiTUxLNTtgGIiIit2LyRgOlRqnZDZLAhpcbEzgcMuVNkbJ/ecFhDLvjltL7G7Ftensp2NV7LlLOXzKW3nPF7vOfiIjI7Zi80cCXNFHqmq+n677ebSHSw6vDAsj5zJ5GPJyiqXbFVDWzTGmdIlyc3Nbz3kZTW3PISFo/n29/G9lLyQsJMvFxYvuDiIjIHkzeKEjPTA36d7izzYSqy0DncIpwchpesIQv3NjnxZn49Fzca42PVhYiN3sKay127KqS3L8ZSWWKQ9O0JriVfnecWpPMjnOIMZSIiCg8TN4YTGuNhEjEKcLJrXhuh8fIfSeVCFdzQerm46c0rFRNDRw7k9uB+93KxIGW5IneJJfXftPE55YR54yXEq5ERER2YfJGA3HdGR89vWkiqQcO692Yx8i7upwmnMxgRqyTS5JLXWQ6Pc6Kz2FxIkDNkKm033WTHTKldF4bGSO1FoK2gpW9XqQSOHb9Bqnp5Ss1sYL4eTVD3dQedzcnTYmIiJyCyZsQxBcIRl8IOP3Cwg4cMkXkHXb3VnLrRaN46E64vW7EjE4shEoGOIne3wqlRJjcOu1I4ITbA1hLbSi7elQRERFFIiZvVFIzi4jUnSu3XjiYxWvdy52K04RTpDPyQtILPSXtOI/dts+kat2o6ZHo1Riptf3C9g4REZG5mLxRULT9gGSjXelOk56aC25r4GrBIVPeFYn72AsX8U6hZT/KxVktw0LCYVUi3pd0VSqYq6eXjZgV567UPlNz3Jwm3Nn5zB66ZdS+ZF0bIiIi52PyJkxqGvUsYmw+r975NBqnCSenMGJWIqNjqp0Xn0q95UIlt+WGlTqtjpWaHqxGUUqaWNkzMZKS3Fp6JhMREZF2TN6oFKrhIVXAWK7XTqQ2YtQ0mPXWuyGyQuDFJ5Ox4VOKhXLPeSl+huqVoaZYcSBxz0Qz6N3/bj5uepJgViVtjIpDao5POD0PGS+JiIjC19DuDXCTnVv3qyrkJ9XFP/B1UsOufP+NxAaO2iFTPhwyZTyn3aF3ArlzMhLPUa1CxTM18c6q3jZOo7V3nNXF3M0ogmuGHbuqLJ1tyslCtV0AdcfH6+ceERGR07HnjQ5qeuGo7T5s9mxWdlJb70Yt311lOosXJuZRexeaPXCkye0Xq5PUWuOplcfSiF43gYkbuXpg4sSNUcltL/1WqeHmBLcT69kUbT9g6PqIiIgiAZM3GoW6g6XU1T+wy7HczFSRfCHIKcKNwRm9jKV0zpKzOfUYiRM3aurd7P3iO0OKFRvB6Ymb7hmxsskxPfFRT60wpW0wg9nHRK5nsRKlZdMzU8PeJiIiokjD5I3B5IZHRTomFNwpUhNjanrEKfWgI2lm7iOt9TicnoBwOid/350wdHbHripLt8Oq46G10DjPMyIiIuMweWMyJzdwzcQpwr2N+/ksTh3uHGoSaOKEm9PprQdmNjfsO1IWKiFtFH5XiIiIjMPkjQ5aL9ikGi++O1K8+JPHIVPG4jTh2oQaAin1eCRfqDi5vowWcsPkrKClHphUvRsfN9dnsYtcfPTtS6/FT7l2CRERETkXkzcWULrDpWccudvonSKcyE5KBXcDl/ElYnnho41TYp7cdmjpxaP1swT2XNM7pFRPzDSzx5xTjiegvZh7qMTM3kZdmBAjIiIi2zF54wBeu+jjkCl34UWJOl47T40U6sLdiAt7I+rZKPU20DoTVjj1zbQmF9QkaqSGTJndM9Ftw9CU6ImDcvuXv0VERERkBiZvLKBmdiq3N3ztEMlDpjhNuHkChzOKZ4ETn6cc9ihNzcxcTot5dm+P1uS2b9iU2iFTZiQU7N5n4Uqr2Ym0mp3Y26iLp4ZFKQ35tGs7OEyciIgofEzeKAg1laWRjRCvNmj0DplSU++G6jNyVq9ITo75iC84lGaYEl+YRNJUuOJ9YcZwUKnEmdplncSqxCvP3/rE8dE3HMroxI3dyXW577/R50Wo81v8fpyVj4iIKDxM3thAy0WIV2mdRcWHQ6bIbHIXF5F4nobD7P2l5iLQrccsMD5qGTJlxxBIt+5jtbzUI8doWqcNJyIiovAweaOgaPsB///L3TFS2w1YPPzCq9TWu1FLXO+G9DHyAoSJMnUC40ckMivehTvTn53E8dHXE0RrfJQbMiVHzzmrdj87bR8D8p9XrjeSnoLESj2bnBQjjRqq5PUkHRERkRsweWMQtQ0buYaUb8aaSKN3yBSHBJCTREJi1g5ysVLNcl6gdxY+I+Kjltm2tDxntu4ZsbI3EeSGlfpq32ghty4nJW4AY2KTntn0vHpOEhER2YnJG5WMunul1JDyWgLHyPorRFYJNxEbKYzuWaQ2USP1OjcmdPTGRzXJbfG6zajBonRsvPRb5pbZ+MzY34GF27ViMpuIiMh4tiZv1q1bhyFDhiAlJQVRUVFYsWJF0POCIGDq1Klo164dGjdujOzsbPz4448h1ztv3jx07NgRcXFxyMrKwqZNmwzfdqkkTDjd+b1wIcgpwt3HLRcmkcjp8dHogsxG17ARF5O2m9lFbJXO5XBjpJ795/WLd6XeTXYULDZjfxvVa8cqRsTM48ePY9SoUYiPj0diYiImTJiAiooKyz4DERGZR0sbeOHChbj66qvRokULtGjRAtnZ2YrL33XXXYiKisLcuXNN2PJzbE3eVFZWIjMzE/PmzZN8/qmnnsLf/vY3LFiwABs3bkTTpk2Rk5OD06dPy67zrbfeQn5+PqZNm4atW7ciMzMTOTk5OHLkiKHbLk626JldhYVR9Yn0IVN2z2TidXouWMxIvro5PioR76dwY6aaYxXOBajZiXW9yW0fK+qBeSERo7unk456YVbfUHBym0Huu2NGTTAjYuaoUaPw3Xff4bPPPsMHH3yAdevWYdKkSYZvKxERWUtrG3jNmjUYOXIkVq9ejYKCAqSmpuK6667Dzz//XG/Zd999Fxs2bEBKSorZH0N78qa4uBiCINR7XBAEFBcXa1rX4MGD8cQTT2DYsGGS65s7dy4effRR3HTTTejRowdef/11HDp0qN7dlEBz5szBxIkTMX78eHTt2hULFixAkyZNsGjRIk3bpoeeZIxSIWQ34xTh1uM04dYy+4LWa/HRxwmJMS2M6H1gRsJVqVhx4PnLnonmkIq3du1rM2ORWeee0T33gPBj5s6dO7Fy5Ur8/e9/R1ZWFq666iq88MILWL58OQ4dOmT49hIRkXW0toHffPNN/OEPf0DPnj2RkZGBv//976irq8OqVauClvv555/xxz/+EW+++SYaNWpk+ufQnLxJS0vD0aNH6z1+/PhxpKWlGbJRALB3716UlJQgOzvb/1hCQgKysrJQUFAg+Zrq6mps2bIl6DXR0dHIzs6WfQ0AVFVV4cSJE0F/ehnZiHJTAodDpiID9/c54p53VvZOcEJ8NOrOeaj9pqZwrptiJVB/lim15JLbgfExnMSr3v3olP1vRpJM7rcnUhLcSuef77g75fgrURMzCwoKkJiYiN69e/uXyc7ORnR0NDZu3Ci5XiPbj0REpJ04BldV1b9W0dsGDvTbb7+hpqYGLVu29D9WV1eH22+/HZMnT0a3btZ0QGio9QWCICAqKqre4xUVFYiLizNkowCgpKQEANC2bdugx9u2bet/TuzYsWOora2VfM2uXbtk32vWrFmYMWNGWNvra7zovYALp35OJImUBrORjJwmPJJoKboqNYSy6tQJfGzKljkjPppx51xKqGPghSE9UlOEK800JU5uy9GabDXq98uJdh1JlOwtE2nxUW8BYiW+WOnk74GamFlSUoKkpKSg5xs2bIiWLVvKxlUj2o9ERJFsX6OL0KxRvObXVTQ6myxPTQ1uj06bNg3Tp08PekxvGzjQQw89hJSUlKAE0JNPPomGDRvinnvu0bz9eqlO3uTn5wMAoqKi8Nhjj6FJkyb+52pra7Fx40b07NnT8A20wpQpU/yfDzibwRN/EZQEXuRJNWA4U0N9HDJF5A7hxsdweT0WBlLqmagmPpqV3Hb6hblTdM+IjajeiYHfiUj9ftgdH4mIIt2BAwcQH38u+RMba3wv3NmzZ2P58uVYs2aNv7PKli1b8Pzzz2Pr1q2SHVvMonrY1LZt27Bt2zYIgoBvv/3W/+9t27Zh165dyMzMxJIlSwzbsOTkZABAaWlp0OOlpaX+58Rat26NBg0aaHoNcPYgx8fHB/0BZ+8sa5mq1vcXeKdY7q6xl3vWcIpwikRS579ZnBAfrWDXkCi747NSrxs7uOXCXClxwt+ls/QcSz09EJ1GTcxMTk6uV7jyzJkzOH78uGyMtDM+EhER6sVgqeSN3jYwADzzzDOYPXs2Pv30U/To0cP/+JdffokjR46gffv2aNiwIRo2bIj9+/fjgQceQMeOHQ35bFJUJ29Wr16N1atXY+zYsfj444/9/169ejU++eQTvPzyy7jwwgsN27C0tDQkJycHFQU6ceIENm7ciL59+0q+JiYmBr169Qp6ja+wkNxrzOaWRm84WO/GOkbWdOA04fLkZpNT8zorOD0+GrUfrK4lFPi+RhGfs3JJBF98DJW4USpUTNrpiYORNnxX7fng5PaOmpjZt29flJWVYcuWLf5lvvjiC9TV1SErK8vybSYiImPobQM/9dRTePzxx7Fy5cqgemgAcPvtt+Obb75BYWGh/y8lJQWTJ0/GJ598Ytpn0VzzZvHixYa9eUVFBXbv3u3/9969e1FYWIiWLVuiffv2uO+++/DEE0/gwgsvRFpaGh577DGkpKRg6NCh/tcMHDgQw4YNQ15eHoCzw7vGjh2L3r17o0+fPpg7dy4qKysxfvx4w7Y7kO9Ou9aLFSc3csymd8hUpDWYyVlC9aoxOnHj5vioZ0YpJ8ZEo7crMHEjTm5rFSq57SSBNeGcdqyVat5o3beReENBrXDrAqoRbszs0qULBg0ahIkTJ2LBggWoqalBXl4ebrvtNkumfyUiIvOEagOPGTMG5513HmbNmgXgbD2bqVOnYtmyZejYsaO/9lmzZs3QrFkztGrVCq1atQp6j0aNGiE5ORnp6emmfQ7NyRsjbd68GQMGDPD/2zdueOzYsViyZAkefPBBVFZWYtKkSSgrK8NVV12FlStXBhVG3rNnD44dO+b/94gRI3D06FFMnToVJSUl6NmzJ1auXFmvQJEWvsaG1MWZ+DEnNUrtwK7p9uA04ebSmpjZuXU/aqorwnpPt8RHIy7GnRo3jZ4iXK5ork84w6XE561TEwlmHWsz6s2k1ex0THLMiuSHmazYbiNi5ptvvom8vDwMHDgQ0dHRGD58OP72t7+Zvu1ERGSuUG3g4uJiREefG5Q0f/58VFdX45Zbbglaj1RBZCtFCYIg2PbuDnXixAkkJCTgvudLEds4ePyy1plPnHaX0WhyQwKUhkwp9bxRGjIVeHHi1AsTs8kNm1K6IJS7s6x1CtxI2udG9KKpqa7Ax4t6o7y83FN1EJTioxpGx0Qnx1ilIVOBMTLUkKnAnomBMVJpinAnna9WHCNf8sbIGAlIx0mpGOmk/e0kcse+6tQJzL23rWfjo9c+FxFZJ1LiiO9zrt68D82a6ZhtquIEBvTu6Pn9JKa65g2dpbUGg1MvKtyGvUH0ibRpcMn5lHoyqqWnHpHVlOpTyQ2ZCjV81Kwpws1mxTHSm7hRoqXXjZH1yMxiVQFw8flpV+FxIiIir2HyRie5JE7glOGRRk0DmVOEkxvZVTiXpLnhWIgTKHrjoxpMbuvvlai1Z6KbBc6IF6qNEk4bRur8dMM5S0RE5HRM3igo2n7A38iRa8goJXDoLLWzTPlwlinzefHCxEjiZA3PaW3U7C87L+asOJ5G9cSQS24zPoYnknslqklGG9m2YeKGiIjIGEzeKEjPTFV1ASfXMAmV+HE7tVOEq8UpcJW5oVu+2ylNEe7V89gMTrhYU+oF6YTtk6oHpkTtkKlI0z0jVjE2KvVKUkpiyyV37KoJZndbQkvdIjWz8hVtP2DIdhEREUUSJm8U+HreAKHvVDnhYsDpOEW4OTjDlzF8FydGncvpmamGrIfUE1802lXMWE29Gzve24tCJU28Eh/tHrppVK0/33OMj0RERNoxeRMGLdOEe/2uvVcayJGOibJz7L7T7UVm709xMWS3J9WZ3DbP3kZdFOvdqJ1lymp6ziGtrzH7PGVcJSIi0ofJG52k7ugqNUjcfhEhpnbIlN4hAazn4Cxe3+9KQyL1nrscFlCf0XHQaRelcnzJbcUpqRWKFWuZZcrr56qUSPrMWs4htQnowGV8bRszzxWvtYeIiIiswuSNTnKNG6X6NxSMd5XNFckFObUQX7hIXfDwYsMc4cZFueMil3QzomaHkXzJba2zTPmS24yPZ+kZKhYJ8dF3HugZ8sR6X0RERM7D5I1B1NRW8GoBYzOnwCUym9qLGyZw7CEVL82KoVYNt9JazN0OUvWDnEgpcSP322R04sbKnj92HAfGPiIiImdg8kYlqcRLYINGzbSbXmkAcciUu3GacPXEMxZ55Rx2CjUJb7nhqWYkwo04vmZNER4qPprNqd/9SBsqZsdx8OJNJyIiIjdi8kYFpcSL0tApuW77Tm0EOw2HBJwTaTPIOIl4WBWgbzgChaZmuFpgfNW6/+24ABX3/tCb3BYzOz6aXffEyaR65ijtb7fH50i6+URERORmTN5oIG7AaClQ7NU7V3qHTKmpd0OhGTnLF5Nlyrx4/lpNbh9q3bd6LyTtvAB1Q40VqeSZV3+7SB9+F4iIiOzD5E2YtNydDJzG1usNILV3lX04ZIrcIvCC1uvnsdGUCgyrqRvmFYHxUUtyW2nIlBExUuqmg5peF0rngdnnSPeMWM09X0INPYukoaVWJU6JiIgofEzeqKA0jXDgf8UXdIEN38Bl3ExtvRu15IYEUHiMvMvPxFkwt5/DTuXFi0I1U4Qr0TJFuBnUHhOl5cw+rno+u9MLFltZpJuIiIjcg8kbldTcZdcypMqLFypS9A6Z4hAeIvJxy4VrqB4gbphlyifUPndKzzOtvW6cPnxNrgeamW0GNckiJxxrIiKiSMfkjUZqEjhqkzhuZ2S9FSJyL7PjnBuS3XqK1jq5Hpj4d8ypv2Vae704fUiU1UkatTeTtEzaEM5yREREJI/JG5209sLxQsOFU4S7n9MvXMidpC7svBDzwiE3y5RevnN315FEW3omio+xGxJqekjFyF1HEmVvVrjp90nLMVOzrJY6VV79vhAREVmJyRsdtBYoFv9/pF/UhMIhU8HcPg0tRSajL9bceodfPEzHl9yW6nUTyO56N4GceOHtK1RsRXxU6mXqtvgsvpnktPOFiIiI5DF5EwYtjR6vNZA4Rbj9OE04RRItd/i9Fm+dyolJnXDI9Uy0q7eTGXzDpHznSDi95rx2/ImIiJyOyZswaa2B41YcMhWZImn/e+E8dTIrEypWH0txfNSbWNWa3Dbr/HRT8ktuH7Amm3529ZojIiIiZUzeGEBNwyRwWnHeGZbnlbubdnL6bCpew6SPOnr2ky9OhhMv7Zx2WZzc1kqc3DYzPmrtSWr3b5gvaaNl2FJazc6IjI9yx8qIY2j394CIiCiSMHkTgpk9Z9za6OGQKaJzAocfMJGjjtqL/8CktxHvKV6v1ZR6JgbGR1/PRCvJ1WgT8xWpdeN3nQXbzzHq+IUzUxURERFpw+SNTdza+CXyOp6X5gunF44eTkiUqx1SKua0hINTzg+lHje6h61p7JWzY1eVK4aW2nXMnHDeEREReQmTNyGEukPsaxR5uZHCejf2MXImE6ddBDqJ+DzXm1zwchzQS26faN3HRl2AGn2M5OrdKA2ZCjXTlBpWxkd+r+vzwu+TmvZLOMfeKYk+IiIir2DyxkC8eDtL75Ap1rtRj8U4ncnXo44XLeELrHdjZFwNNSTIDlLxMdQU4VZy2/fZit8St00RLsc3DE6O2449ERGRlzF5E6ZIq3fBpIH3MGlWH5OwoRVtP6BqOb1xUVzkXS8trw83hhsRH+Xq3fA8VU/vcfBi70Q1M2ISERGROzB5E4Kahr/aQo9uxCFTkSsSjoHRvToiKemTnpmqajm1+9jsfWdlbFaqnaJlyJSdyQTx8XDad9vo+LS3URfNNW+c0PvGiOSMlhnZQp3PRswQR0RERNIcn7zp2LEjoqKi6v3l5uZKLr9kyZJ6y8bFxYW9HU7sau8lvKtsjEicBjccUud1uOezlcMnnRIfjSA+Fm4Yhmr0xbs4uW0n8fHwQn23UPHRjT1vzEpKdrm0g+Sx1tqLzcrvy7p16zBkyBCkpKQgKioKK1as8D9XU1ODhx56CBdffDGaNm2KlJQUjBkzBocOHQpah1RMnT17tmWfgYiIzDNv3jx07NgRcXFxyMrKwqZNm2SX/e677zB8+HD/78LcuXMll/v5558xevRotGrVCo0bN8bFF1+MzZs3m/QJXJC8+frrr3H48GH/32effQYAuPXWW2VfEx8fH/Sa/fv1NR7U3lkG3N2gVYtThJMXubm3nJ3xUcyXbBHHQqmkjBpuPi4+WnsmyhEntyOhV5wbWH0c9LYzQvWikjvXtD5ut8rKSmRmZmLevHn1nvvtt9+wdetWPPbYY9i6dSveeecdFBUV4cYbb6y37MyZM4Ni5B//+EcrNp+IiEz01ltvIT8/H9OmTcPWrVuRmZmJnJwcHDlyRHL53377DZ06dcLs2bORnJwsucyvv/6KK6+8Eo0aNcLHH3+M77//Hs8++yxatGhh2udoaNqaDdKmTZugf8+ePRudO3dGv379ZF8TFRUlu5PNFFj/JhLovTDhkCnrufGOshXMSrp2ubQDqk6dwMemrP0cJ8VHM+rKODmWys0yJRYYH42YZcpuTjkmeno96RkaJdcr1I7fqXDrR4W7Hqe9l9jgwYMxePBgyecSEhL8yW2fF198EX369EFxcTHat2/vf7x58+a2tCGJiMg8c+bMwcSJEzF+/HgAwIIFC/Dhhx9i0aJFePjhh+stf9lll+Gyyy4DAMnnAeDJJ59EamoqFi9e7H8sLS3NhK0/x/E9bwJVV1dj6dKluOOOOxAVFSW7XEVFBTp06IDU1FTcdNNN+O475QZzVVUVTpw4EfQnRWv9G7cLVe9GK7lCnCTNCfUUSJ+dW/erLuhrFLvjoxI746LVvSK1xke5nolMuMrTExvTanYqHhs3DDm16rts5pBFJ/VSLi8vR1RUFBITE4Menz17Nlq1aoVLLrkETz/9NM6cOSO7DiPiIxER6SeOwVVV9W+uVFdXY8uWLcjOzvY/Fh0djezsbBQUFOh+7/fffx+9e/fGrbfeiqSkJFxyySVYuHCh7vWp4arkzYoVK1BWVoZx48bJLpOeno5Fixbhvffew9KlS1FXV4crrrgCBw8elH3NrFmzkJCQ4P9LTT07XEru4stLCRojcYpwa3DGL5JidXxUYkTdIC3rU7rYtCte+3omqiVX78as+GjGRbQTLsytiI929Q616rvsu1GlVLQ41ONOOx/FTp8+jYceeggjR45EfHy8//F77rkHy5cvx+rVq/G///u/+Otf/4oHH3xQdj164iMREZ3z49FE7Dqi/e/Ho4kAgNTU1KA4PGvWrHrvcezYMdTW1qJt27ZBj7dt2xYlJSW6t/2nn37C/PnzceGFF+KTTz7B3XffjXvuuQevvfaa7nWG4qrkzauvvorBgwcjJSVFdpm+fftizJgx6NmzJ/r164d33nkHbdq0wcsvvyz7milTpqC8vNz/d+DAuaSNUmPU18AJbIy4ocimHkwYeFOkJ86cciFhBDvio5SdW/eHvV/FMdXs9wuXG+uBhdpnRtVXMVqoXjdSMU1NoWKpnk5Oio92tCvkig6Helz8/05TU1OD3//+9xAEAfPnzw96Lj8/H/3790ePHj1w11134dlnn8ULL7wgeScX0B4fiYjIWAcOHAiKw1OmTLHsvevq6nDppZfir3/9Ky655BJMmjQJEydOxIIFC0x7T8fXvPHZv38/Pv/8c7zzzjuaXteoUSNccskl2L17t+wysbGxiI2VbxBKXRw4uWFiBE4RHtl4HNzFzvgopiU2GpXosfrCVu2wHa3x0UdpyJRV56ae4+LU30Xf/pRK4rhleJrczHhW7HO3FS5W4kvc7N+/H1988UVQrxspWVlZOHPmDPbt24f09PR6z2uNj0REZKz4+PiQsbx169Zo0KABSktLgx4vLS0Nq8ZZu3bt0LVr16DHunTpgn//+9+61xmKa3reLF68GElJSbjhhhs0va62thbffvst2rVrp/u9fRcHobrmR0IPHC04ZMpabqjZQOawMz6qpTceyg3hUDtjjpXEyW3FZSXioy+5LXcuM6kaeh9o7SUaqhaOkznhO+8mvsTNjz/+iM8//xytWrUK+ZrCwkJER0cjKSnJgi0kIiIzxMTEoFevXli1apX/sbq6OqxatQp9+/bVvd4rr7wSRUVFQY/98MMP6NDBvN9nVyRv6urqsHjxYowdOxYNGwZ3FhozZkxQ96iZM2fi008/xU8//YStW7di9OjR2L9/P+68805d7x1u48gLCRy9QwKI3MDtF0B2xkctjNjPgfHUKcfNjPjolt4gdjCjkLvW/e3mYvJa2yRuasNUVFSgsLAQhYWFAIC9e/eisLAQxcXFqKmpwS233ILNmzfjzTffRG1tLUpKSlBSUoLq6moAQEFBAebOnYvt27fjp59+wptvvon7778fo0ePNnXaVyIiMl9+fj4WLlyI1157DTt37sTdd9+NyspK/+xT4jZzdXW1/zeluroaP//8MwoLC4N6q99///3YsGED/vrXv2L37t1YtmwZXnnlFeTm5pr2OVwxbOrzzz9HcXEx7rjjjnrPFRcXIzr6XA7q119/xcSJE1FSUoIWLVqgV69eWL9+fb0uTXqIe9ZIXTyIH3NTw8eHQ6a8hReC5rBjuI4Up8RHo6kdrurUIVNu4YSaQVaQ613jtfho9PGUW5cTvzebN2/GgAED/P/Oz88HAIwdOxbTp0/H+++/DwDo2bNn0OtWr16N/v37IzY2FsuXL8f06dNRVVWFtLQ03H///f71EBGRe40YMQJHjx7F1KlTUVJSgp49e2LlypX+IsbiNvOhQ4dwySXnhrU/88wzeOaZZ9CvXz+sWbMGwNnpxN99911MmTIFM2fORFpaGubOnYtRo0aZ9jmiBEEQTFu7S504cQIJCQkYfMdmNIpp5n9cqRGj9LzbiC9OfHeWlZI3SsU45ZI34iFTTN6co3SBKHenX8/FidywtUg9FloSAaGSNzXVFfh4UW+Ul5eHHIvrJr74eN/zpYhtbP/nsvoiUmt8lOt1Eyo+AsHnp1nnpNrfLyddrGuNj0rDovTER8A9MVJ83IwYbmhEm6fq1AnMvbetZ+Oj1z4XEVknUuKI73O+8nEZGjfV/jlPVZ7ApMGJnt9PYq4YNuVkvoaRE+4Im8GNs6h4mZGzfrHeEIJqWXnhfI1ETkkoGM2q89Or+y+QUoJGT70bJyVuQsUtqd5rvse0zOomtU4tr2F8JSIiCh+TNxpIXeDJJW283lBRO2TKh0OmyKn0Xrx6/RwPpWh78JS4aqb0prO0Jrftio9OrDGkxMjktpvoObfCnUFT7qaV2vcjIiIi7Zi8MYlbGypq692oJZ4Cl8xh5GwpkZhIc+v5aqf0zFRNy+upB6a0jNoLViPW4SM3ZMpHb3wMldw2k9I+8Mp5oXfYlFuSQU45TpFw44qIiMhOTN7oEO5Fh5foHTLFITvkBL66NUaer1rvSLuVuOeNnjv34Syj9v2MWEco4uRAqHo3oYjjo5lFkt30fe2eEatpX6iZBlzpeTf8TjlpyHbgdylS2kBERERWYvLGBL4GjFsaxHLccteRiKyXnpnqqAs0q7dFb3xkPTDyIifFAiIiIq9i8kYFJ93ZMhOnCPcer02D6waRNHRAS4LazftEqbeHXK8btXzxUc6OXVWeipHhfA+07odQ8W9voy6yy0TCTHxGnZNS61GKDW6OBURERHZi8kYFuYYGGyD6uKErup3MHCJBZ8mdu27vLecFeuNqqKnbwxV4Xkr1uhHXuwk1ZEpcDyxSEq1OOseUhkzJ9axySny2sv2hZkYrFi8mIiIyH5M3YfLqXXZOEe48nCbcWF49d+0WbmIsnAs7sxM4ZD+je994EZMjRERE3sTkDQHgkCk6K1KOh9zFTbgX/l6odRUus4dShUq6OWX/KyWynTALn9lJLjPWr7VgsRpyvW+sTnBr3V96v+d6pn9Xuxx7KRMREZmLyRuyFHt8GM/IacIpfLxQUU/vBahjEjQKyW01PROlktuBMdLMZKqZ+3Dn1v2mrF9pf0j1TFQTG53SM8eK77TUcTF6pj0iIiIyD5M3Icg1RrzcSOGQKSJ9mLgxn5djr1d4+RhFSu9EH8Y0IiIi52DyRkF6ZioAdQ3RSGjgaJ1FhezllDvKFLmkhjiZFSvNWq+eYTp6k9vsmajM6CFTWmeaMlvgd9iM80aqLROqfaNmtk01BY3VLktERETymLxRyct3EtXWu1GL9W70c8pMJkRGkKoBZFYsNatYsThOGVU4PFR8pPDtbdTFVcNKA88Nq84bPcItKk5ERET6MHmjoGj7gaB/8+7RWbyrbD0jZ5oiMpPe2GhWz4JwaU1ua50i3EccH52U3I6k3zul3yk7jomds+KpeV+z6hsRERFRfUzehCDuxuy7k6ymK7EbMUngfUyincULjvBZdWHphtiqNKxUTz0wJ/XCc/q5YtXvlpHHRO132s4Z7NS8r9oeb+JlxDfHiIiIKDQmb1TwXaBIdWN2eqM2FE4RTj6Rekzcfg7bTdwjMdT+tDIRY/R7ySUJtNYD88VHp3ND0kyJniFTcsd4x64qQ2NkpMUd8ef11RQkIiIi9Zi8USEwSSPVmI20Rpge7O1hDjfVc3Ainrv6FW0/oKsuh5X73Kz3CrcemI8vuS0VH81OpqpJzDjt/JDaJ17qLRrYk81NiTO1PZHd9JmIiIiciMkbFXwNKt9dZaUEjpsbJ5winCJRuOes0y5wraL3zrnc/lbaj27ex06Nj26cRVGqp6gZNwakfgutGMYWeKNIy3feaccJ0DezFRERESlj8kaDUI0qLwyjCqR1yJQPh0zZjzPYWMeJF05O5qZEt9QFe7i93dw0y5Sbf89CHSel/e+2nqJ2HSc3nMNERERewuSNSm5uxMoJVe9GK7lZVEgdJxUojRS8+LCGWcNNzTx+4vNR3BtDKrkdaqYptZjgrs/u+GjmMZH7Hhv9/XZCvLNz9iwiIiK3Y/JGJTeOQzcapwi3h5dqOlBkMiv5LVdrI9w4HSpxI8WoKcKtoGYfOem3Tilxoyc+uqHXE6B+JqdQxLNmql02FD3ntRdvhBEREVmFyRsNnNSYNRoTBJGBiTQyih1T/SrFYN9zXhu+apZQiYFI3Yd2xEgr6z2FWp8Vxz1Sv1tEREThYvImQnGKcAoUacclsOeB0Rf7kXJhIi5YbNXQD7l1G7nf1Z4PoeKj1mLFRk9HLUdPUVwtjDzmdg+X8nHKdmhlRK9hO85tIiIiqo/JGx0iscHCIVPOw2nC9Qu8aDW6BkMkxgdAuZC71e9ppMBeiXJThKsdMuWmYsXhMPq4GJ3QkoudXvu9Eieo9RwX3yybUiIlUU1EROQUDe3eALIfh0wRkZUCLyrNWN4t7EwWKF2UO42RvV72NuoSMYlvtxxfIiIiUoc9b8LkxrvsHDLlbV6/q+90kXjBJBUHfT2a5GKk1sSNlQJjpC/BIr7gN3rIVLi07ic1xXDd+PsWSlrNTs0x0uzhbE7ez2rOUydvPxERkZew500IvoYLGyfaea0LupncWk/BrayqwxIppC7wjCrCqrSsGT1y1E4RrpWa5Hb3jFjdSQIzZv5xQiLSt1/sFM5xUcMJ+zkcbt9+IiIit2DPGwXigpxepHcKXKvvKkcqI4e0MZl2Dmck8gYnHEM3TRFOzuX2pK9SLzurPtu6deswZMgQpKSkICoqCitWrAh6fty4cYiKigr6GzRoUNAyx48fx6hRoxAfH4/ExERMmDABFRUVlmw/ERFRKI5O3kyfPr3eD21GRobia95++21kZGQgLi4OF198MT766KOwtsHXIBEX/DO6yKnTqB0y5cMhU+7FY+NeToiR4dASQwOXNWuWqVC9btTSm9x26rlo92+d3H7Renz2Nuri2GGlTkhEygm3vWPVZ6usrERmZibmzZsnu8ygQYNw+PBh/98//vGPoOdHjRqF7777Dp999hk++OADrFu3DpMmTTJ704mIyALz5s1Dx44dERcXh6ysLGzatElx+VBt5oqKCuTl5eH8889H48aN0bVrVyxYsMDMj+Ds5A0AdOvWLeiH9quvvpJddv369Rg5ciQmTJiAbdu2YejQoRg6dCh27Nih672Lth8AUL/hIf633Q1bLdTWu1FL7q4ymStSCm5SaHbGSCsFxl27L3TDTW67rdeNnfu7e0asYcOmvB43zWqLqOmpKLWM1W2jwYMH44knnsCwYcNkl4mNjUVycrL/r0WLFv7ndu7ciZUrV+Lvf/87srKycNVVV+GFF17A8uXLcejQISs+AhERmeStt95Cfn4+pk2bhq1btyIzMxM5OTk4cuSI5PJq2sz5+flYuXIlli5dip07d+K+++5DXl4e3n//fdM+h+OTNw0bNgz6oW3durXsss8//zwGDRqEyZMno0uXLnj88cdx6aWX4sUXXzR8uwJ74th9IWEmThFO5GxOjZFSxBdzZsbOcC8c1UwRLoVDSu1lRoLGqT2ijCBXbFzP68Sc2DZas2YNkpKSkJ6ejrvvvhu//PKL/7mCggIkJiaid+/e/seys7MRHR2NjRs3Sq6vqqoKJ06cCPojIiLnmTNnDiZOnIjx48f7e8g0adIEixYtklxeTZt5/fr1GDt2LPr374+OHTti0qRJyMzMDNmjJxyOT978+OOPSElJQadOnTBq1CgUFxfLLltQUIDs7Oygx3JyclBQUKD4HqF+fL06RIpThFMkkpsFyYkXGmqYHSONvDixch/bdTwDE95qeyaakRww6jfLjb99ehI4cjccvJy4AbQVGw+Mm26Ml4MGDcLrr7+OVatW4cknn8TatWsxePBg1NbWAgBKSkqQlJQU9JqGDRuiZcuWKCkpkVznrFmzkJCQ4P9LTfV+rUQiIicRt1Grqur/bldXV2PLli1BbeDo6GhkZ2fLtoHVtJmvuOIKvP/++/j5558hCAJWr16NH374Adddd51Bn64+R882lZWVhSVLliA9PR2HDx/GjBkzcPXVV2PHjh1o3rx5veVLSkrQtm3boMfatm0r+6PrM2vWLMyYMSPk9vhq37gVpwh3JiNnUnFqPQcnkZsi2Y0XqVbESLXx0evUDikV97wJFR/NYtRvld2/eVpmmwqn101GUpnjeoxqbXOYfazs/i6E67bbbvP//8UXX4wePXqgc+fOWLNmDQYOHKhrnVOmTEF+fr7/3ydOnGACh4hIg+9/qEJsY+3XilWnzr5GHHOnTZuG6dOnBz127Ngx1NbWSraBd+3aJbl+NW3mF154AZMmTcL555+Phg0bIjo6GgsXLsQ111yj+fOo5eieN4MHD8att96KHj16ICcnBx999BHKysrwz3/+09D3mTJlCsrLy/1/Bw4cMHT9XsIhU0TOYUWMjPT4KE4IBCa3lWaaclo9MDcmJwH55HY4PUfdUvvG6GSJXd8Bp373OnXqhNatW2P37t0AgOTk5Hq1D86cOYPjx48jOTlZch2xsbGIj48P+iMiIuscOHAgqJ06ZcoUy977hRdewIYNG/D+++9jy5YtePbZZ5Gbm4vPP//ctPd0dM8bscTERFx00UX+H1qx5ORklJaWBj1WWloq+6PrExsbi9hY43o/uIHeKcLJOpwmnLQyI0YaFR+d3nMxMElgZnwUn4tW9UxUGgrj5OOiha93k1xyxve8VC8op8RIM49HuOv10ncFAA4ePIhffvkF7dq1AwD07dsXZWVl2LJlC3r16gUA+OKLL1BXV4esrCw7N5WIiGSoSZy3bt0aDRo00NQGDtVmPnXqFB555BG8++67uOGGGwAAPXr0QGFhIZ555pl6Q66M4uieN2IVFRXYs2eP/4dWrG/fvli1alXQY5999hn69u1rxeY5GodMkRQeH29xcox040Wf1h4abixW7Mbjopcbetw4+XjonU3Kqs9UUVGBwsJCFBYWAgD27t2LwsJCFBcXo6KiApMnT8aGDRuwb98+rFq1CjfddBMuuOAC5OTknN3OLl0waNAgTJw4EZs2bcJ///tf5OXl4bbbbkNKSooln4GIiIwXExODXr16BbWB6+rqsGrVKtk2cKg2c01NDWpqahAdHZxOadCgAerq6gz+BOc4Onnzpz/9CWvXrsW+ffuwfv16DBs2DA0aNMDIkSMBAGPGjAnqGnXvvfdi5cqVePbZZ7Fr1y5Mnz4dmzdvRl5enmHb5NTuv07hlLuXXuaGCxCnc/IFkhZOjJFq6ImjdsZercltMa/VorL7d9DoYvv83TpH7bH1LRe4vN3fi82bN+OSSy7BJZecvbmUn5+PSy65BFOnTkWDBg3wzTff4MYbb8RFF12ECRMmoFevXvjyyy+Deha++eabyMjIwMCBA3H99dfjqquuwiuvvGLXRyIiIoPk5+dj4cKFeO2117Bz507cfffdqKysxPjx4wFobzPHx8ejX79+mDx5MtasWYO9e/diyZIleP311zFs2DDTPoejh00dPHgQI0eOxC+//II2bdrgqquuwoYNG9CmTRsAQHFxcVC264orrsCyZcvw6KOP4pFHHsGFF16IFStWoHv37nZ9BEfSOyTAjXeVieTIFS52EzfGSLXTEFuZYDNryJSvZ6JPYJLAib3e1O53u5Ofu44kWjJbohOPkRXUfA98zweez3Z/L/r37w9BEGSf/+STT0Kuo2XLlli2bJmRm0VERA4wYsQIHD16FFOnTkVJSQl69uyJlStX+osS62kzL1++HFOmTMGoUaNw/PhxdOjQAX/5y19w1113mfY5HJ28Wb58ueLza9asqffYrbfeiltvvdWU7fHCxZ4So+8qR2rD1y5eu7tPoTktRqqhNzlg94VhKIHJbacVK/YaKxI3kcxJM1wREREZJS8vT7a3uZ42c3JyMhYvXmzU5qni6GFTTtHl0g7+BoobGypq692oJa53Q/oZOU04EWkX6hzUGx99vJJU9fKNCzmMz0REROQkTN5otHPrfs83YvUOmWLdAOPwzrL5vH4eU2haLs6VeiZKxcdQyW0nJgaUbk445cZFqNjo9ZpgUnErVCzTEuvMOs6Mt0REROFj8kajwF44bsSkQORhUo28zKyLQq8nAZSorUtkpu4ZsYYnuJzUC0rv/nPjkEIiIiIyBpM3HscpwklOJB8jqQsnXgCFx64760YeN3FyW2rIlNpixWLiJKqTzz83FC2WIpVwCydh4+RjpIXR9frE61Kzbid+X4iIiNyGyZsQ2OBQh707rBHJvQGMInehwW794ZGKlW4dZio+z7Qmt32c1NMjUoWKmXb9dlndttD6fkrnrnhdbCcRERFZg8kbldx4ASLGKcKJyEpGDDN1Y+xlMXfzRMqNArXfezPPD6npwLVQel3R9gO61klERBTJmLwJwXf3yY13ljhkKnLwDr96bjyX3cLsnjZmrlvvkCk9yW2nxkgjeqUZcYz01rrREwftrAMXuK/E+01tnNIbz0Id68D16n0PpdelZ6bqWicREVEkY/JGJTfe/SVnc+JsM5GCCRxzuKmge6jkto/a5LaYL5Gw60hiUG8RpyZuAPmhb+GuQys9+yitZqehw0qtOE56EiThtkWkkjNy22TG+xMREZF+TN6QH6cIdwbOCOYNXr+zbNWwB6WLSSddSIp7JtrJyP0S7tAZoxkdH932+6VUfNiMwsFOOe5ERETE5I2i9MxU19xFDoUJgcjktgsTL/F6TQcjk1N6h+Xoic9SU1C7tR6Y3DA1I3rQiNdn9IxFRlMaMuW1YaVGH18t76X3e+Dk7w4REZFbMHkTgq/ejduSOKx34z1uGxLgdG47p53KiIsyLfU9wj1uSt/9cGeZMqvXjdKsP2bVRlGbFHIKpfiYVrPTcwkcKXLHx+jEiZ7vgbgHl9eT20RERGZg8kZBJDUuOGSKiPRw8gW9XuJixVLxUUlgvRsj2LGPvXhctXBbTbLA46W3+LEZ5LbF68NKiYiIzMDkTQTgkCmic3xDTdiN3x2MPk56LsqlEtlWzDJlxhTNWtdh9XkiNawtXHp63bi5d6JUIifU8WRMJCIicj4mbzyIQ6YiSyQMBzCSG4dBOpFUz0Q1F3+hljH7AlIcp3zJbaVhN1p63njlfPSdI1afK3pnmrJ7G8xgxpBEvbNMGfX+REREpB+TN6Qbh0zp57Yu+URKQk0/rOUiVNxrwMiLPzXnXbjJbSMZ8dnd2KPCquSJ0m+YE2K0nuOvVB/JiPdT831y2/eNiIjILZi8UcHNDRG3zqISqYwc4sbkmn26XNohomo6hJpOWu9FqNF37fUkBbTWuzHyvY1iVBJIz3NaaRk2lVazU3evm1Dx0Sm9b7Qy6pyROqZubgsRERF5AZM3IXh1iIXau8o+HDLlHTxW5tu5db/nC55LJafUTCNs1uxIalnVo8JryVOl42HXb6QZQ9ScEB+NrFmkd31SxzSwPcREDhERkfWYvFHgxjvnauvdqOUbEkD2MrqmA53j1QStXdTuSzuG84jjo1y9m1DJbV/PRHF8dGK9G6njId7veo6DWcdOKXki1TNRzT7XEj/NKJislZZ4FKq3m7gHm1LB4lDLBK5by3kuxevJbSIiIjMweRPhOEU4RarAixveRTaO2n3pxKSZ3inCxfVuxPHR6t4cofarUhFbtUWnnXbs5Oxt1MWRSTWjqK1TE7i81DEOXI+Rx1ZuXW68OUZERGQ3Jm88jFOEE8ljwsa5rKi3oTc+er0emFGFbfXS2uuFvRKVSSVJ9fSa0Vt0nIiIiIzD5I0Ct3Xr5RTh7mBkl3wv31Emd9E6C43SsnL1NsIR6rzTOqTUJ1R8VPPe4TIrEWnE8Co3Cfwts3L4lBm9/7SuT6k3jpt6WhEREXlZQ7s3wOkirdHCIVNEpIfWQqZOjatak9uhmJncNjuZYvUxsrvWjPj9rboxYdRsYOEMfXJiQWoiIiIKxuSNgvTMVMQ2jrd7M3TRO0U42YfThBNZI/Bc0zLsxmnJbc78oyySeiYywUJEROR9HDblERwyRWrweJHZ3HgHX2rIlJbktt1JAiNmJzL6ffTQOtOUXkoJth27qvx/ZPy05URERKQfkzekCXt0WI8FOcmJ5GqCOfFCTe1wHL3JbSN5bSiUneRiJ4v5q2fk98WJsYGIiMhNmLxR4LaCxT56h0x5fRYVokCRdBFrBrmpfvXsV7su6oxOjBoxRTi/l/Ux2aKdEUWQjSpY7ftO87tNREQUHiZvIojau8piHDLlTHYP1fACqWl0yXpWHgNxIkDtLFNMbttLT69PuRgpty63/KaFM2ubFnqmGPdtD3vZEBERGc/RyZtZs2bhsssuQ/PmzZGUlIShQ4eiqKhI8TVLlixBVFRU0F9cXJzubXBDA0RtvRu1zBwSEOnsnk2FzGdlQsgJMdLpQsVHn8DktlK9G7X1wJzKyb9pcsdq15FEXb1vvDrkVE18sSoGib9PWhI8Rlu3bh2GDBmClJQUREVFYcWKFUHPi+Oe7+/pp5/2L9OxY8d6z8+ePduU7SUiImvNmzcPHTt2RFxcHLKysrBp0ybF5d9++21kZGQgLi4OF198MT766KOg5wVBwNSpU9GuXTs0btwY2dnZ+PHHH838CM5O3qxduxa5ubnYsGEDPvvsM9TU1OC6665DZWWl4uvi4+Nx+PBh/9/+/foaCumZqZ68K693yBTr3ZiHwwLs4+SL2VDsjJGBw0qNGKLhJXYVvFVzDLz4mxapxNODa3mdEe8NyH+f7ChcXllZiczMTMybN0/y+cCYd/jwYSxatAhRUVEYPnx40HIzZ84MWu6Pf/yjKdtLRETWeeutt5Cfn49p06Zh69atyMzMRE5ODo4cOSK5/Pr16zFy5EhMmDAB27Ztw9ChQzF06FDs2LHDv8xTTz2Fv/3tb1iwYAE2btyIpk2bIicnB6dPnzbtczh6qvCVK1cG/XvJkiVISkrCli1bcM0118i+LioqCsnJyWZvniMxCUBMsqlnRsLByiSGnTEysOaN2ouxUBd8ZvPFR6UeGW5Obuu9kA/1Or1JAi2UeiXq/V1zW68orbScd4HLdrm0g65jGu73wOzv0eDBgzF48GDZ58Ux77333sOAAQPQqVOnoMebN28esW1IIiKvmjNnDiZOnIjx48cDABYsWIAPP/wQixYtwsMPP1xv+eeffx6DBg3C5MmTAQCPP/44PvvsM7z44otYsGABBEHA3Llz8eijj+Kmm24CALz++uto27YtVqxYgdtuu82Uz+Honjdi5eXlAICWLVsqLldRUYEOHTogNTUVN910E777TnnK16qqKpw4cSLozy04RTipxWPmfWbESCPjo9U1hkINUxTHRy3TgwPy8dHs4ZFG9ZwQX0zLrZe9dYxjRw81qeOn55iKE0BGbIddSktL8eGHH2LChAn1nps9ezZatWqFSy65BE8//TTOnDkjux43tx+JiLxAHIOrqupf71RXV2PLli3Izs72PxYdHY3s7GwUFBRIrregoCBoeQDIycnxL793716UlJQELZOQkICsrCzZdRrB0T1vAtXV1eG+++7DlVdeie7du8sul56ejkWLFqFHjx4oLy/HM888gyuuuALfffcdzj//fMnXzJo1CzNmzDBr0z3BCXeVI5FXazaQ8cyKkUrx0YoeGW7ghvgo1ftCTOoxtx5jp/a6UdqXbtzXUglAqYSg0z7Xa6+9hubNm+Pmm28Oevyee+7BpZdeipYtW2L9+vWYMmUKDh8+jDlz5kiuh+1HIqLwFG0/gEYxzTS/rqa6AgCQmho8++m0adMwffr0oMeOHTuG2tpatG3bNujxtm3bYteuXZLrLykpkVy+pKTE/7zvMbllzOCanje5ubnYsWMHli9frrhc3759MWbMGPTs2RP9+vXDO++8gzZt2uDll1+Wfc2UKVNQXl7u/ztwgFOEE5G7mBUjjYiPXqmHo7aYu1G93MLpCWPUPnfaRbeX2HlemPH9kEs+OfE7tGjRIowaNapesfb8/Hz0798fPXr0wF133YVnn30WL7zwguSdXMA77UciIrc6cOBAUByeMmWK3ZtkKlckb/Ly8vDBBx9g9erVsr1n5DRq1AiXXHIJdu/eLbtMbGws4uPjg/7cgEOmIpdT7yq7jRMvKvQwM0YqxUc37T+9dVO0JredEh/1TPPsNnp6Jir1knLKbIBGJVbMnEZc7Tqd+L378ssvUVRUhDvvvDPksllZWThz5gz27dsn+bxb249ERF4hjsGxsfV/y1u3bo0GDRqgtLQ06PHS0lLZGmfJycmKy/v+q2WdRnB08kYQBOTl5eHdd9/FF198gbS0NM3rqK2txbfffot27dqZsIWRwQ1DAtzAKRcG5B2MkfL0TBGuxI4pwp144esEbhtOKpVE8T0W+Jze4y2e7c2soUpqZpVzQy+7V199Fb169UJmZmbIZQsLCxEdHY2kpCQLtoyIiMwQExODXr16YdWqVf7H6urqsGrVKvTt21fyNX379g1aHgA+++wz//JpaWlITk4OWubEiRPYuHGj7DqN4OiaN7m5uVi2bBnee+89NG/e3D9+LCEhAY0bNwYAjBkzBueddx5mzZoF4OwUj5dffjkuuOAClJWV4emnn8b+/ftV3WHxOg6ZcibOEOYtvgumbzZoK36rh1tipBOTEL6eiT5aixX7uDW57cb6Kj57G3UxPIETbo8ppf2ptUeK1uNiVS8r3yxVWrbFShUVFUE9CPfu3YvCwkK0bNkS7du3B3C2Yf3222/j2Wefrff6goICbNy4EQMGDEDz5s1RUFCA+++/H6NHj0aLFi0s+xxERGS8/Px8jB07Fr1790afPn0wd+5cVFZW+mefEreX7733XvTr1w/PPvssbrjhBixfvhybN2/GK6+8AuDszK333XcfnnjiCVx44YVIS0vDY489hpSUFAwdOtS0z+Ho5M38+fMBAP379w96fPHixRg3bhwAoLi4GNHR5zoQ/frrr5g4cSJKSkrQokUL9OrVC+vXr0fXrl2t2mxbMAFAgHsvJL0oPTMVH5v8HoyR6hgVH0PVunGSUMkZNyRurP5d654RqzuJ44b9GUhv8s7Jn3Pz5s0YMGCA/9/5+fkAgLFjx2LJkiUAgOXLl0MQBIwcObLe62NjY7F8+XJMnz4dVVVVSEtLw/333+9fDxERudeIESNw9OhRTJ06FSUlJejZsydWrlzpLzgsbi9fccUVWLZsGR599FE88sgjuPDCC7FixYqgSUEefPBBVFZWYtKkSSgrK8NVV12FlStX1qunZqQoQRAE09buUidOnEBCQgLue74UsY2dOX5ZPCTA18hVqnej1PNGaUhAYELAKfUc3EhuGIfcBYrSnWW5IRtyyRseN3lmdfPv1KUF5t7bFuXl5Z6qg6AmPjqhV0fg+RZ4jqXV7NQcH4GzMTKS4qNVx1BpOKme2OijNUb6uP0YOuHcU6Pq1AlPx0evfS4isk6kxBHf5xx8x2bds019vKi35/eTmKNr3pBx9A6ZYk8O8jo3XOi4jd37VG29G59QQ6Z8yW2nMqMWid3HEJD//dnbqItizSE99YiMTNrYWffFCcdNiRtq4hARETkVkzcewCFTRETGsiO5rffCtsulHUJetIundPYCrUkaq3rdOD2BYhevfO+IiIjswuSNC3GK8MjGacKNZcYFBS/etDPrwk48pDRcZp5/Vn1vxO9j50X1jl1Vsr8tSjcm0mp2yh5Tt81GFS6l2ayMWJ+adalZRk2SkYiIiOQxeUOyOGTKOJwmPHLw4iSY2otIo/ZbqHMt3OS2jzg+GpHclpruORIwPobH6GSc1woZExEReQWTNy6nZsgUpwh3LiOHvDHZpg/vBpvLzn3rtviod1+pqXljxvuazQsxzY4knNExTW5dRvf4ISIiImVM3niU2rvKPhwy5V08dtYK90KanCdUfDSD0sV34HfM91+nJmBCCWe2KbnjofU4GR0jlc5/L8UGqR4/vse89DmJiIicgskbl1Fb70Ytp8+iEqkirWaD17j1QtqrvBYfA3tWGN3LwkkX3aF63hgVJ40etuWFBIaebQ/8HjIGEhERGY/JG4/jFOFE1uJFi76Cp0ZQexHu65noxvho9JTg4tc46furZ1ipXK8bq4+ZXI+oLpd2MKQAsBW9e4x4DzcnsIiIiJyGyRsX4xThROQ0UhdrdiQErIyPThiaGM7wKSclbMyi9H0w4/gp9YgKNSQu1DKhnld7PJUSK+G8PxEREZmDyRsX4RTh7mRkl3xOE05OZ1cB6FDx0SxWx0c9CQGn0hMbjT6eTvl9c2LtIiO2xUmfh4iIyO2YvIkwTh8SQKSHUV3zeaFhLCfNRmNUcpvx0VhOSZ6YSe47b/eQIjvjXdH2A7a9NxERkVsxeeNSeqfAJefgNOHOwsRNeOwcLiXuwWHlFOFGF7vVekEvnnXKTXbsqpLdf0bHR7tiZOAMTEqPhzpXnHp81c6uJ14mPTPVrE0iIiLyLCZvXIJDpkiPSDl+viKgTr3AiQRqEzVS0wuHK9T33MxZpow6x3zfXzX1UAIFzjpFzmPUMDe55c2MeWrWrXaYpHgZ9rwhIiLSjskbCsIeHPbjNOH6GHXxyotg46ktjGr0haj4XFJKbgf2uvEt50tuO4XXvptaey2piY1a4qddyW0t3/PAZb2UnGbPGyIiIu2YvHEhK4cEELmJ2ml45YTbe8drF9fh0ro/9e6/wCSAEcNtxD1v5OrduKFnm5cu+NVwQ1F3uWFUcsKZFSoUM9dNRERExmLyxkPUDpny4ZApd3HDRYlT6L1g5cWKsXxDKrhfQ5PaT0YMBXTqvje6VpDbqZn6W++xjLQEHhERkVcxeeMCauvdqKWlngOFhxco1jLiIsWpF7teY/YFpdKQKT3F3O0YUmr0d9Gpsx6pZXQC2w3x2ezvgBEJIbd8f4iIiNyOyRsP0jtkivVurGPkTCokjRcU1lJ7Med7zqokmd7kdqieiW5kVPFcs1kRH73Qs1RtMk7trFZWDHEkIiIi/Zi8cRle9JMYk27nmDHshNRRe4Fo9EWf1t4Tenrd2Mmoi22n0ZM8MbKYuxt63eil9xxjQoaIiMjZmLxxOE4RTnpF6jHUWgyU3EscH+WS26Hio7hnoppeN044v3zfdb2zFzmV3oS0E3pJhUoYq93/aqfpDnd7iIiIyD2YvCEA7L3hFJwm3Bjh3kFmkV1jWX3xqLcemFpOSNzo5YTvdfeMWFOmCdfKjONoROww8nzRsz1a31/L+plIIiIi0o/JGxfhFOFE6kkNoSJ7OCFhoEazXpf4e93YIVJmSVNKmmQklRk6PNhpNybUHCtxwsXq2GXm98lt31UiIiInYfLGwThkinycMBzAC5jAMY/V+zZUzw2pXjdaktuB55yVCQCjZvHx2nc9VAzUGiPd8Dunp7i3Ucdd68xkXvu+ERERORGTN+S4O5Ne4eWCmG4Rbv0b3iV2Dz31bgITN77ktphb46MXv7tKCRqtw6rcGJ+NqoOj5n20rseL3zciIiKnYfLGJThkyju8PCTAicKtQcGLEnW0JsqMvFMfeE6ZXTfKzB4b4u+q0d89N/SOUIppeo6tm2do7HJph6BjZlUssnK2OCIiIlKPyRsiigjiGhJuuJB1sqLtB0Iu46SLQKnkttumDQ+X3D638nzQ2+NFLnHj643jxqGlVvWkMRPjKBERkXWYvHEo1ruJPEb2GOBxlKa3EKjTL6DskJ6Zavcm1KM1Pqqpd2MEsy9w1SZf5JaR651mxnbv2FWF7hmxsjFKa0+ZtJqdisdLridPpMRIM46h2t5AvuWY4CEiIjIGkzceoXfIFIfdUCQKvFhlAofEyW05ei/4QyVGjChSbMb30+3feaf/vlmxf42YJjwwOailHo7V359Zs2bhsssuQ/PmzZGUlIShQ4eiqKgoaJnTp08jNzcXrVq1QrNmzTB8+HCUlpYGLVNcXIwbbrgBTZo0QVJSEiZPnowzZ85Y+VGIiIgkuSJ5M2/ePHTs2BFxcXHIysrCpk2bFJd/++23kZGRgbi4OFx88cX46KOPLNpSc7h5zD6Rk/mSOGp7Ljj1YtZpMdLsYTiBPRP1xke764EF1jORqnOjdv/pKS7rBHYWDI6UXjd6SPVMVKrD5KReNWvXrkVubi42bNiAzz77DDU1NbjuuutQWVnpX+b+++/Hf/7zH7z99ttYu3YtDh06hJtvvtn/fG1tLW644QZUV1dj/fr1eO2117BkyRJMnTrVjo9EREQ2OH78OEaNGoX4+HgkJiZiwoQJqKioUHyNmpsDX3/9NQYOHIjExES0aNECOTk52L59u6Ztc3zy5q233kJ+fj6mTZuGrVu3IjMzEzk5OThy5Ijk8uvXr8fIkSMxYcIEbNu2DUOHDsXQoUOxY8cOi7dcPw6ZokBurOXgJGqTMlruJjvpYtkJMVJqH1uxj9QmbsQ9E6USN6HioxmULoQjsSeNVTcqnDrTlJMSIYBy0ibU41LLmP35Vq5ciXHjxqFbt27IzMzEkiVLUFxcjC1btgAAysvL8eqrr2LOnDn43e9+h169emHx4sVYv349NmzYAAD49NNP8f3332Pp0qXo2bMnBg8ejMcffxzz5s1DdXW1qdtPRETOMGrUKHz33Xf47LPP8MEHH2DdunWYNGmS4mtC3RyoqKjAoEGD0L59e2zcuBFfffUVmjdvjpycHNTU1KjeNscnb+bMmYOJEydi/Pjx6Nq1KxYsWIAmTZpg0aJFkss///zzGDRoECZPnowuXbrg8ccfx6WXXooXX3zR4i13Pqd3KXczp14c0DlKvUN8zzntYkqKE2Kk+AJOzQWd3n1rdHJbzq4jiZbHyHCSK1p66pC11NYjcnpyTQsnxM/y8nIAQMuWLQEAW7ZsQU1NDbKzs/3LZGRkoH379igoKAAAFBQU4OKLL0bbtm39y+Tk5ODEiRP47rv6w9Orqqpw4sSJoD8iInKvnTt3YuXKlfj73/+OrKwsXHXVVXjhhRewfPlyHDp0SPI1am4O7Nq1C8ePH8fMmTORnp6Obt26Ydq0aSgtLcX+/ep/Lxsa8ilNUl1djS1btmDKlCn+x6Kjo5Gdne3/oRUrKChAfn5+0GM5OTlYsWKF7PtUVVWhqupcrxPfD37V6ZNhbL1+pyqDL04qKs42Bk7WnO36W/vbKQBAZdXZu0D71tYvdNuxXxec/L/n6/5v+d8qzr6+otGJ/3uf4Nxd1Sn2vDGK+Bj6XNimDHK97nzHV8x3vMR+PJoIQPo5HstzOnVpgapT9fdTpy4tAEDxObnnxctILeuLH4IgaNtgDayIkWbFx5rqCtl9qyTw3PLFRkA+PgJAhahexbefbkfHfmd72DTt2QMnfjtVLz6e3+TE/51jZ33/gzPPqU5dWuCbDd8hPTPV/18leve7keTiY+DxFJOLj4B8jBT/xvkYeSyLth8Iuc/lYpDaZZTeQ+o5NdtkBKX3qamuQHpmatBnCvzumR0f6+rqcN999+HKK69E9+7dAQAlJSWIiYlBYmJi0LJt27ZFSUmJf5nAxI3ved9zYrNmzcKMGTPqPc4kDhHp5YsfZrYfneRMtfJwpFCvE8fb2NhYxMbqv4leUFCAxMRE9O7d2/9YdnY2oqOjsXHjRgwbNqzea0LdHLj88suRnp6OVq1a4dVXX8UjjzyC2tpavPrqq+jSpQs6duyoevscnbw5duwYamtrJX9Id+3aJfkauR9eqR9dH7kf3/kPXaBjqx1i4xa7t4CIAJw8eRIJCQmmrNuKGGlmfPw47DWEwWMx8mPRf9UsS8awYn8qvYfUc1Yd43C3y6z4mJubix07duCrr74yfN2BpkyZEpQM//nnn9G1a1ekpjpvNj4ichcz249OEBMTg+TkZHy2tL/udTRr1qxevJ02bRqmT5+ue50lJSVISkoKeqxhw4Zo2bKlbFtZzc2B5s2bY82aNRg6dCgef/xxAMCFF16ITz75BA0bqk/JODp5YxXxj29ZWRk6dOiA4uJiV500J06cQGpqKg4cOID4+Hi7N0cVN24zwO22khu3GTh7x+TkyZNISUmxe1PCwvhoLzdutxu3GeB2W8nM+JiXl+evUXD++ef7H09OTkZ1dTXKysqCGtilpaVITk72LyMu+O4rOOlbJpD4Dm+zZs3w/fffo2vXrq46HqG48Tumhhc/Fz+Te8h9Lq+0H0OJi4vD3r17w6onJggCoqKigh6T63Xz8MMP48knn1Rc386d9UezGOXUqVOYMGECrrzySvzjH/9AbW0tnnnmGdxwww34+uuv0bhxY1XrcXTypnXr1mjQoEG9Ss2BP7RiycnJmpYH5LtXJSQkuDJIxMfHu2673bjNALfbSm7cZrOTG1bESMZHZ3DjdrtxmwFut1WMjo+CIOCPf/wj3n33XaxZswZpaWlBz/fq1QuNGjXCqlWrMHz4cABAUVERiouL0bdvXwBA37598Ze//AVHjhzx33n97LPPEB8fj65du4bchujoaJx33nkA3Hc81PDiZwK8+bn4mdxD6nO56eZYOOLi4hAXF2fJez3wwAMYN26c4jKdOnVCcnJyvUk/zpw5g+PHjyu2rUPdHFi2bBn27duHgoICREdH+x9r0aIF3nvvPdx2222qPoejCxbHxMSgV69eWLVqlf+xuro6rFq1yv9DK9a3b9+g5YGzP7xyyxMRuRVjJBHRWbm5uVi6dCmWLVuG5s2bo6SkBCUlJTh16mwdrISEBEyYMAH5+flYvXo1tmzZgvHjx6Nv3764/PLLAQDXXXcdunbtittvvx3bt2/HJ598gkcffRS5ublh1VAgIiJ7tWnTBhkZGYp/MTEx6Nu3L8rKyvwzFQLAF198gbq6OmRlZUmuO/DmgI/45sBvv/2G6OjooJ5Cvn/X1dWp/hyOTt4AQH5+PhYuXIjXXnsNO3fuxN13343KykqMHz8eADBmzJigYp333nsvVq5ciWeffRa7du3C9OnTsXnzZuTl5dn1EYiITMMYSUQEzJ8/H+Xl5ejfvz/atWvn/3vrrbf8yzz33HP4f//v/2H48OG45pprkJycjHfeecf/fIMGDfDBBx+gQYMG6Nu3L0aPHo0xY8Zg5syZdnwkIiKyWJcuXTBo0CBMnDgRmzZtwn//+1/k5eXhtttu8w9l+/nnn5GRkeEfZqvm5sC1116LX3/9Fbm5udi5cye+++47jB8/Hg0bNsSAAQPUb6DgAi+88ILQvn17ISYmRujTp4+wYcMG/3P9+vUTxo4dG7T8P//5T+Giiy4SYmJihG7dugkffvihpvc7ffq0MG3aNOH06dNGbL5l3LjdbtxmQeB2W8mN22w1K2OkW48Ht9s6btxmQeB2kzG8eDy8+JkEwZufi5/JPbz6ubzgl19+EUaOHCk0a9ZMiI+PF8aPHy+cPHnS//zevXsFAMLq1av9j506dUr4wx/+ILRo0UJo0qSJMGzYMOHw4cNB6/3000+FK6+8UkhISBBatGgh/O53vxMKCgo0bVuUIETIPGRERERERERERC7k+GFTRERERERERESRjMkbIiIiIiIiIiIHY/KGiIiIiIiIiMjBmLwhIiIiIiIiInIwJm9EbrzxRrRv3x5xcXFo164dbr/9dhw6dChomW+++QZXX3014uLikJqaiqeeesqmrT1r3759mDBhAtLS0tC4cWN07twZ06ZNQ3V1ddByTtvuv/zlL7jiiivQpEkTJCYmSi5TXFyMG264AU2aNEFSUhImT56MM2fOWLuhEubNm4eOHTsiLi4OWVlZ/qninGDdunUYMmQIUlJSEBUVhRUrVgQ9LwgCpk6dinbt2qFx48bIzs7Gjz/+aM/GBpg1axYuu+wyNG/eHElJSRg6dCiKioqCljl9+jRyc3PRqlUrNGvWDMOHD0dpaalNWxx5GB+tw/hoHjfGSMZHd3D6d1/MiHPh+PHjGDVqFOLj45GYmIgJEyagoqLCwk9xjlHnidNi6/z589GjRw/Ex8cjPj4effv2xccff+x/3o2fSWz27NmIiorCfffd53/MjZ9r+vTpiIqKCvrLyMjwP+/Gz0QOo3cKLa+aM2eOUFBQIOzbt0/473//K/Tt21fo27ev//ny8nKhbdu2wqhRo4QdO3YI//jHP4TGjRsLL7/8sm3b/PHHHwvjxo0TPvnkE2HPnj3Ce++9JyQlJQkPPPCAo7d76tSpwpw5c4T8/HwhISGh3vNnzpwRunfvLmRnZwvbtm0TPvroI6F169bClClTrN/YAMuXLxdiYmKERYsWCd99950wceJEITExUSgtLbV1u3w++ugj4c9//rPwzjvvCACEd999N+j52bNnCwkJCcKKFSuE7du3CzfeeKOQlpYmnDp1yp4N/j85OTnC4sWLhR07dgiFhYXC9ddfL7Rv316oqKjwL3PXXXcJqampwqpVq4TNmzcLl19+uXDFFVfYuNWRhfHROoyP5nFjjGR8dD43fPfFjDgXBg0aJGRmZgobNmwQvvzyS+GCCy4QRo4cafEnOcuI88SJsfX9998XPvzwQ+GHH34QioqKhEceeURo1KiRsGPHDkEQ3PmZAm3atEno2LGj0KNHD+Hee+/1P+7GzzVt2jShW7duwuHDh/1/R48e9T/vxs9EzsLkTQjvvfeeEBUVJVRXVwuCIAgvvfSS0KJFC6Gqqsq/zEMPPSSkp6fbtYmSnnrqKSEtLc3/bydv9+LFiyUvTj766CMhOjpaKCkp8T82f/58IT4+PuhzWK1Pnz5Cbm6u/9+1tbVCSkqKMGvWLNu2SY64MVZXVyckJycLTz/9tP+xsrIyITY2VvjHP/5hwxbKO3LkiABAWLt2rSAIZ7ezUaNGwttvv+1fZufOnQIAoaCgwK7NjGiMj+ZjfDSXW2Mk46PzuO27L6bnXPj+++8FAMLXX3/tX+bjjz8WoqKihJ9//tmybZej5zxxamwVa9GihfD3v//d9Z/p5MmTwoUXXih89tlnQr9+/fzJG7d+rmnTpgmZmZmSz7n1M5GzcNiUguPHj+PNN9/EFVdcgUaNGgEACgoKcM011yAmJsa/XE5ODoqKivDrr7/atan1lJeXo2XLlv5/u2W7AxUUFODiiy9G27Zt/Y/l5OTgxIkT+O6772zZpurqamzZsgXZ2dn+x6Kjo5GdnY2CggJbtkmLvXv3oqSkJGj7ExISkJWV5bjtLy8vBwD/93jLli2oqakJ2vaMjAy0b9/ecdseCRgf7cX4aA63xEjGR2fxwndfTM25UFBQgMTERPTu3du/THZ2NqKjo7Fx40bLt1lMz3nixNgaqLa2FsuXL0dlZSX69u3r+s+Um5uLG264IWj7AXcfqx9//BEpKSno1KkTRo0aheLiYgDu/kzkHEzeSHjooYfQtGlTtGrVCsXFxXjvvff8z5WUlASdUAD8/y4pKbF0O+Xs3r0bL7zwAv73f//X/5gbtlvMidt87Ngx1NbWSm6XU/djIN82On376+rqcN999+HKK69E9+7dAZzd9piYmHr1P5y27V7H+OgMTtxmt8dHwB0xkvHRebzw3RdTcy6UlJQgKSkp6PmGDRuiZcuWtn9uveeJE2MrAHz77bdo1qwZYmNjcdddd+Hdd99F165dXf2Zli9fjq1bt2LWrFn1nnPr58rKysKSJUuwcuVKzJ8/H3v37sXVV1+NkydPuvYzkbNERPLm4Ycfrlc8Svy3a9cu//KTJ0/Gtm3b8Omnn6JBgwYYM2YMBEFw/HYDwM8//4xBgwbh1ltvxcSJE12xzURiubm52LFjB5YvX273pnge46Ozt5lIjPGRKDSvnSfp6ekoLCzExo0bcffdd2Ps2LH4/vvv7d4s3Q4cOIB7770Xb775JuLi4uzeHMMMHjwYt956K3r06IGcnBx89NFHKCsrwz//+U+7N408oqHdG2CFBx54AOPGjVNcplOnTv7/b926NVq3bo2LLroIXbp0QWpqKjZs2IC+ffsiOTm5XlVw37+Tk5Nt3e5Dhw5hwIABuOKKK/DKK68ELWfVdmvdZiXJycn1Zmowa1+r1bp1azRo0EByX9q1TVr4trG0tBTt2rXzP15aWoqePXvatFXB8vLy8MEHH2DdunU4//zz/Y8nJyejuroaZWVlQXct3LLvnYrxkfHRKG6Pj4DzYyTjozN54bsvpuZcSE5OxpEjR4Jed+bMGRw/ftzWzx3OeeLE2AoAMTExuOCCCwAAvXr1wtdff43nn38eI0aMcOVn2rJlC44cOYJLL73U/1htbS3WrVuHF198EZ988okrP5dYYmIiLrroIuzevRvXXnutJz4T2czuojtOt3//fgGAsHr1akEQzhW29BXoFARBmDJliu2FLQ8ePChceOGFwm233SacOXOm3vNO3W5BCF2QM3CmhpdfflmIj48XTp8+beEWBuvTp4+Ql5fn/3dtba1w3nnnObIoIWQKED7zzDP+x8rLyx1RjLOurk7Izc0VUlJShB9++KHe875Cb//617/8j+3atYsFOW3E+Gg+xkdzuSVGMj46n9u++2J6zgVfweLNmzf7l/nkk09sK1hsxHni1NgqNmDAAGHs2LGu/UwnTpwQvv3226C/3r17C6NHjxa+/fZb134usZMnTwotWrQQnn/+ec98JrIXkzcBNmzYILzwwgvCtm3bhH379gmrVq0SrrjiCqFz587+E6asrExo27atcPvttws7duwQli9fLjRp0sTWKWUPHjwoXHDBBcLAgQOFgwcPBk1P5+PE7d6/f7+wbds2YcaMGUKzZs2Ebdu2Cdu2bRNOnjwpCMK56fKuu+46obCwUFi5cqXQpk0b26fLW758uRAbGyssWbJE+P7774VJkyYJiYmJQZXh7XTy5En/vgQgzJkzR9i2bZuwf/9+QRDOTv2ZmJgovPfee8I333wj3HTTTbZPgysIgnD33XcLCQkJwpo1a4K+w7/99pt/mbvuukto37698MUXXwibN2+uN1U1mYfx0VqMj+ZxY4xkfHQ+N3z3xYw4FwYNGiRccsklwsaNG4WvvvpKuPDCC22bKtyI88SJsfXhhx8W1q5dK+zdu1f45ptvhIcffliIiooSPv30U0EQ3PmZpATONiUI7vxcDzzwgLBmzRph7969wn//+18hOztbaN26tXDkyBFBENz5mchZmLwJ8M033wgDBgwQWrZsKcTGxgodO3YU7rrrLuHgwYNBy23fvl246qqrhNjYWOG8884TZs+ebdMWn7V48WIBgORfIKdt99ixYyW32XcXXxAEYd++fcLgwYOFxo0bC61btxYeeOABoaamxr6N/j8vvPCC0L59eyEmJkbo06ePsGHDBrs3yW/16tWS+3Xs2LGCIJy9M/XYY48Jbdu2FWJjY4WBAwcKRUVF9m60IMh+hxcvXuxf5tSpU8If/vAHoUWLFkKTJk2EYcOGBV2Ek3kYH63F+GgeN8ZIxkd3cPp3X8yIc+GXX34RRo4cKTRr1kyIj48Xxo8f708yW82o88RpsfWOO+4QOnToIMTExAht2rQRBg4c6E/cCII7P5MUcfLGjZ9rxIgRQrt27YSYmBjhvPPOE0aMGCHs3r3b/7wbPxM5S5Qg2FBpkoiIiIiIiIiIVImI2aaIiIiIiIiIiNyKyRsiIiIiIiIiIgdj8oaIiIiIiIiIyMGYvCEiIiIiIiIicjAmb4iIiIiIiIiIHIzJGyIiIiIiIiIiB2PyhoiIiIiIiIjIwZi8ISIiIiIiIiJyMCZviIiIiIiIiIgcjMkbIiIiIiIiIiIHY/KGiIiIiIiIiMjBmLwhUuno0aNITk7GX//6V/9j69evR0xMDFatWmXjlhER2YvxkYhIHmMkERkhShAEwe6NIHKLjz76CEOHDsX69euRnp6Onj174qabbsKcOXPs3jQiIlsxPhIRyWOMJKJwMXlDpFFubi4+//xz9O7dG99++y2+/vprxMbG2r1ZRES2Y3wkIpLHGElE4WDyhkijU6dOoXv37jhw4AC2bNmCiy++2O5NIiJyBMZHIiJ5jJFEFA7WvCHSaM+ePTh06BDq6uqwb98+uzeHiMgxGB+JiOQxRhJRONjzhkiD6upq9OnTBz179kR6ejrmzp2Lb7/9FklJSXZvGhGRrRgfiYjkMUYSUbiYvCHSYPLkyfjXv/6F7du3o1mzZujXrx8SEhLwwQcf2L1pRES2YnwkIpLHGElE4eKwKSKV1qxZg7lz5+KNN95AfHw8oqOj8cYbb+DLL7/E/Pnz7d48IiLbMD4SEcljjCQiI7DnDRERERERERGRg7HnDRERERERERGRgzF5Q0RERERERETkYEzeEBERERERERE5GJM3REREREREREQOxuQNEREREREREZGDMXlDRERERERERORgTN4QERERERERETkYkzdERERERERERA7G5A0RERERERERkYMxeUNERERERERE5GBM3hAR/f/24JAAAAAAQND/194wAAAAAGMB5lCJ/t7/uHAAAAAASUVORK5CYII=", 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" ] }, - "metadata": { - "needs_background": "light" + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(ncols=3, figsize=(15, 4))\n", + "\n", + "im0 = axes[0].contourf(data['x'], data['t'], np.real(data['u']), cmap='coolwarm')\n", + "axes[0].set_xlabel('x')\n", + "axes[0].set_ylabel('t')\n", + "axes[0].set_title('Ground truth')\n", + "\n", + "im1 = axes[1].contourf(data['x'], data['t'], y_noisy.reshape(data['x'].shape), cmap='coolwarm')\n", + "axes[1].set_xlabel('x')\n", + "axes[1].set_title('Noisy')\n", + "\n", + "sampled = np.array([y_noisy[index, 0] if index in idx[:number_of_samples] else np.nan for index in np.arange(data['x'].size)])\n", + "sampled = np.rot90(sampled.reshape(data['x'].shape)) #array needs to be rotated because of imshow\n", + "\n", + "im2 = axes[2].imshow(sampled, aspect='auto', cmap='coolwarm')\n", + "axes[2].set_xlabel('x')\n", + "axes[2].set_title('Sampled')\n", + "\n", + "fig.colorbar(im1, ax=axes.ravel().tolist())\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], @@ -238,12 +276,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "## Running DeepMoD\n", - "config = {'n_in': 2, 'hidden_dims': [20, 20, 20, 20, 20, 20], 'n_out': 1, 'library_function': library_1D_in, 'library_args':{'poly_order': 1, 'diff_order': 3}}" + "config = {'n_in': 2, 'hidden_dims': [20, 20, 20, 20, 20, 20], 'n_out': 1, 'library_function': library_1D_in, 'library_args':{'poly_order': 2, 'diff_order': 3}}" ] }, { @@ -255,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -287,18 +325,20 @@ "output_type": "stream", "text": [ "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 25000 100.00% 0s 5.83e-05 3.13e-05 4.58e-06 2.24e-05 \n", + " 25000 100.00% 0s 1.41e-04 1.13e-04 4.45e-06 2.37e-05 \n", "[Parameter containing:\n", "tensor([[-0.7742],\n", - " [-4.7608]], requires_grad=True)]\n", + " [-3.9990]], requires_grad=True)]\n", "[tensor([3, 5])]\n", "\n", "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", - " 25000 100.00% 0s 9.18e-05 2.87e-05 6.32e-05 0.00e+00 " + " 25000 100.00% 0s 5.34e-04 1.14e-04 4.20e-04 0.00e+00 CPU times: user 28min 42s, sys: 17.6 s, total: 29min\n", + "Wall time: 28min 59s\n" ] } ], "source": [ + "%%time\n", "train_deepmod(model, X_train, y_train, optimizer, 25000, {'l1': 1e-5})" ] }, @@ -311,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -328,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -337,7 +377,7 @@ "text": [ "Parameter containing:\n", "tensor([[-0.7742],\n", - " [-4.7608]], requires_grad=True)\n" + " [-3.9990]], requires_grad=True)\n" ] } ], @@ -345,6 +385,13 @@ "print(model.fit.coeff_vector[0])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "My main question is how am I to know how to interpret sparsity mask in terms of the terms in the equation" + ] + }, { "cell_type": "code", "execution_count": null, @@ -369,7 +416,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/examples/PDE_Keller_Segel.ipynb b/examples/PDE_Keller_Segel.ipynb new file mode 100644 index 0000000..8cfe939 --- /dev/null +++ b/examples/PDE_Keller_Segel.ipynb @@ -0,0 +1,426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example Korteweg de Vries equation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we provide a simple example of the DeepMoD algorithm by applying it on the Burgers' equation. \n", + "\n", + "We start by importing the required libraries and setting the plotting style:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# General imports\n", + "import numpy as np\n", + "import torch\n", + "import matplotlib.pylab as plt\n", + "# DeepMoD stuff\n", + "import sys\n", + "sys.path.append('../src/')\n", + "from deepymod_torch.DeepMod import DeepMod\n", + "from deepymod_torch.library_functions import library_1D_in\n", + "from deepymod_torch.training import train_deepmod, train_mse\n", + "\n", + "# Settings for reproducibility\n", + "np.random.seed(42)\n", + "torch.manual_seed(0)\n", + "\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we prepare the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = np.load('../../DeePyMoD/examples/data/keller_segel.npy', allow_pickle=True).item()\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n", + "\n", + "im = axs[0].contourf(data['x'], data['t'], np.real(data['u']))\n", + "axs[0].set_xlabel('x')\n", + "axs[0].set_ylabel('t')\n", + "fig.colorbar(mappable=im, ax=axs[0])\n", + "\n", + "im = axs[1].contourf(data['x'], data['t'], np.real(data['v']))\n", + "axs[1].set_xlabel('x')\n", + "axs[1].set_ylabel('t')\n", + "fig.colorbar(mappable=im, ax=axs[1])\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot it to get an idea of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10201, 2) (10201, 2)\n" + ] + } + ], + "source": [ + "X = np.transpose((data['t'].flatten(), data['x'].flatten()))\n", + "y = np.concatenate((np.real(data['u']).reshape((data['u'].size, 1)), np.real(data['v']).reshape((data['v'].size, 1))), axis=1)\n", + "print(X.shape, y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, $X$ has 2 dimensions, $\\{x, t\\}$, while $y$ has only one, $\\{u\\}$. Always explicity set the shape (i.e. $N\\times 1$, not $N$) or you'll get errors. This dataset is noiseless, so let's add $5\\%$ noise:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "noise_level = 0\n", + "y_noisy = y + noise_level * np.std(y) * np.random.randn(y[:,0].size, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset is also much larger than needed, so let's hussle it and pick out a 1000 samples:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "number_of_samples = 10000\n", + "\n", + "idx = np.random.permutation(y.shape[0])\n", + "X_train = torch.tensor(X[idx, :][:number_of_samples], dtype=torch.float32, requires_grad=True)\n", + "y_train = torch.tensor(y_noisy[idx, :][:number_of_samples], dtype=torch.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([10000, 2]) torch.Size([10000, 2])\n" + ] + } + ], + "source": [ + "print(X_train.shape, y_train.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a dataset which we can use. Let's plot, for a final time, the original dataset, the noisy set and the samples points:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(ncols=3, figsize=(15, 4))\n", + "\n", + "im0 = axes[0].contourf(data['x'], data['t'], np.real(data['u']), cmap='coolwarm')\n", + "axes[0].set_xlabel('x')\n", + "axes[0].set_ylabel('t')\n", + "axes[0].set_title('Ground truth')\n", + "\n", + "im1 = axes[1].contourf(data['x'], data['t'], y_noisy[:,0].reshape(data['x'].shape), cmap='coolwarm')\n", + "axes[1].set_xlabel('x')\n", + "axes[1].set_title('Noisy')\n", + "\n", + "sampled = np.array([y_noisy[index, 0] if index in idx[:number_of_samples] else np.nan for index in np.arange(data['x'].size)])\n", + "sampled = np.rot90(sampled.reshape(data['x'].shape)) #array needs to be rotated because of imshow\n", + "\n", + "im2 = axes[2].imshow(sampled, aspect='auto', cmap='coolwarm')\n", + "axes[2].set_xlabel('x')\n", + "axes[2].set_title('Sampled')\n", + "\n", + "fig.colorbar(im1, ax=axes.ravel().tolist())\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(ncols=3, figsize=(15, 4))\n", + "\n", + "im0 = axes[0].contourf(data['x'], data['t'], np.real(data['v']), cmap='coolwarm')\n", + "axes[0].set_xlabel('x')\n", + "axes[0].set_ylabel('t')\n", + "axes[0].set_title('Ground truth')\n", + "\n", + "im1 = axes[1].contourf(data['x'], data['t'], y_noisy[:,1].reshape(data['x'].shape), cmap='coolwarm')\n", + "axes[1].set_xlabel('x')\n", + "axes[1].set_title('Noisy')\n", + "\n", + "sampled = np.array([y_noisy[index, 1] if index in idx[:number_of_samples] else np.nan for index in np.arange(data['x'].size)])\n", + "sampled = np.rot90(sampled.reshape(data['x'].shape)) #array needs to be rotated because of imshow\n", + "\n", + "im2 = axes[2].imshow(sampled, aspect='auto', cmap='coolwarm')\n", + "axes[2].set_xlabel('x')\n", + "axes[2].set_title('Sampled')\n", + "\n", + "fig.colorbar(im1, ax=axes.ravel().tolist())\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuring DeepMoD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now setup the options for DeepMoD. The setup requires the dimensions of the neural network, a library function and some args for the library function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "## Running DeepMoD\n", + "config = {'n_in': 2, 'hidden_dims': [20, 20, 20, 20, 20, 20], 'n_out': 2, 'library_function': library_1D_in, 'library_args':{'poly_order': 1, 'diff_order': 2}}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we instantiate the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "model = DeepMod(**config)\n", + "optimizer = torch.optim.Adam([{'params': model.network_parameters(), 'lr':0.001}, {'params': model.coeff_vector(), 'lr':0.0025}],betas=(0.99, 0.99))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run DeepMoD " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now run DeepMoD using all the options we have set and the training data. We need to slightly preprocess the input data for the derivatives:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", + " 25000 100.00% 0s 5.04e-05 5.87e-06 3.03e-06 4.15e-05 \n", + "[Parameter containing:\n", + "tensor([[-8.9342],\n", + " [ 0.4236],\n", + " [-4.4185],\n", + " [-4.5267]], requires_grad=True), Parameter containing:\n", + "tensor([[0.0942],\n", + " [0.5134]], requires_grad=True)]\n", + "[tensor([ 7, 9, 13, 15]), tensor([2, 5])]\n", + "\n", + "| Iteration | Progress | Time remaining | Cost | MSE | Reg | L1 |\n", + " 25000 100.00% 0s 6.44e-05 5.82e-06 5.86e-05 0.00e+00 CPU times: user 1h 36min 14s, sys: 36.4 s, total: 1h 36min 51s\n", + "Wall time: 1h 37min 3s\n" + ] + } + ], + "source": [ + "%%time\n", + "train_deepmod(model, X_train, y_train, optimizer, 25000, {'l1': 1e-5})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that DeepMoD has converged, it has found the following numbers:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[tensor([ 7, 9, 13, 15]), tensor([2, 5])]\n" + ] + } + ], + "source": [ + "print(model.fit.sparsity_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Parameter containing:\n", + " tensor([[-8.9342],\n", + " [ 0.4236],\n", + " [-4.4185],\n", + " [-4.5267]], requires_grad=True),\n", + " Parameter containing:\n", + " tensor([[0.0942],\n", + " [0.5134]], requires_grad=True))" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit.coeff_vector[0], model.fit.coeff_vector[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "My main question is how am I to know how to interpret sparsity mask in terms of the terms in the equation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.17" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/test.ipynb b/examples/test.ipynb new file mode 100644 index 0000000..840ff2e --- /dev/null +++ b/examples/test.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "mnist = tf.keras.datasets.mnist\n", + "\n", + "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n", + "x_train, x_test = x_train / 255.0, x_test / 255.0\n", + "\n", + "def create_model():\n", + " return tf.keras.models.Sequential([\n", + " tf.keras.layers.Flatten(input_shape=(28, 28), name='layers_flatten'),\n", + " tf.keras.layers.Dense(512, activation='relu', name='layers_dense'),\n", + " tf.keras.layers.Dropout(0.2, name='layers_dropout'),\n", + " tf.keras.layers.Dense(10, activation='softmax', name='layers_dense_2')\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-19 13:22:06.121795: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.\n", + "2023-10-19 13:22:06.121833: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.\n", + "2023-10-19 13:22:06.506406: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.\n", + "2023-10-19 13:22:06.506605: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1748] CUPTI activity buffer flushed\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + " 1/1875 [..............................] - ETA: 6:06 - loss: 2.4212 - accuracy: 0.0312" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-19 13:22:06.974034: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.\n", + "2023-10-19 13:22:06.974072: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 67/1875 [>.............................] - ETA: 12s - loss: 0.8793 - accuracy: 0.7411" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-19 13:22:07.251452: I tensorflow/core/profiler/lib/profiler_session.cc:66] Profiler session collecting data.\n", + "2023-10-19 13:22:07.251797: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1748] CUPTI activity buffer flushed\n", + "2023-10-19 13:22:07.263377: I tensorflow/core/profiler/internal/gpu/cupti_collector.cc:673] GpuTracer has collected 68 callback api events and 65 activity events. \n", + "2023-10-19 13:22:07.265665: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.\n", + "2023-10-19 13:22:07.269267: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07\n", + "\n", + "2023-10-19 13:22:07.271575: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for trace.json.gz to logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07/hal5.trace.json.gz\n", + "2023-10-19 13:22:07.274743: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07\n", + "\n", + "2023-10-19 13:22:07.275741: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for memory_profile.json.gz to logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07/hal5.memory_profile.json.gz\n", + "2023-10-19 13:22:07.277781: I tensorflow/core/profiler/rpc/client/capture_profile.cc:251] Creating directory: logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07\n", + "Dumped tool data for xplane.pb to logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07/hal5.xplane.pb\n", + "Dumped tool data for overview_page.pb to logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07/hal5.overview_page.pb\n", + "Dumped tool data for input_pipeline.pb to logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07/hal5.input_pipeline.pb\n", + "Dumped tool data for tensorflow_stats.pb to logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07/hal5.tensorflow_stats.pb\n", + "Dumped tool data for kernel_stats.pb to logs/fit/20231019-132206/train/plugins/profile/2023_10_19_13_22_07/hal5.kernel_stats.pb\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2217 - accuracy: 0.9342 - val_loss: 0.1039 - val_accuracy: 0.9682\n", + "Epoch 2/5\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0990 - accuracy: 0.9698 - val_loss: 0.0788 - val_accuracy: 0.9745\n", + "Epoch 3/5\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0702 - accuracy: 0.9784 - val_loss: 0.0767 - val_accuracy: 0.9763\n", + "Epoch 4/5\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0544 - accuracy: 0.9829 - val_loss: 0.0713 - val_accuracy: 0.9784\n", + "Epoch 5/5\n", + "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0424 - accuracy: 0.9858 - val_loss: 0.0723 - val_accuracy: 0.9800\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = create_model()\n", + "model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", + "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n", + "\n", + "model.fit(x=x_train, \n", + " y=y_train, \n", + " epochs=5, \n", + " validation_data=(x_test, y_test), callbacks=[tensorboard_callback]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UsageError: Line magic function `%tensorboard` not found.\n" + ] + } + ], + "source": [ + "%tensorboard --logdir logs/fit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py3.10tf", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/deepymod_torch/DeepMod.py b/src/deepymod_torch/DeepMod.py index 411c141..0f37199 100644 --- a/src/deepymod_torch/DeepMod.py +++ b/src/deepymod_torch/DeepMod.py @@ -4,20 +4,64 @@ class DeepMod(nn.Module): - ''' Class based interface for deepmod.''' + '''Module subclass for data-driven discovery of partial differential equations. + + This module implements a neural network architecture for discovering the governing equations of a system + from data. The architecture consists of a fully connected neural network followed by a library of candidate + functions and a sparse regression layer. The library of candidate functions is defined by a user-provided + function and its arguments. + + Args: + n_in (int): Number of input features: the number of temporal+spatial dimensions. + hidden_dims (list of int): List of dimensions for the hidden layers of the neural network. + n_out (int): Number of output features (the number of equations to discover). + library_function (callable): Function that generates the library of candidate functions. + library_args (tuple or dict): Arguments to pass to the library function. + + Attributes: + network (nn.Sequential): The fully connected neural network. + library (Library): The library of candidate functions. + fit (Fitting): The sparse regression layer. + ''' def __init__(self, n_in, hidden_dims, n_out, library_function, library_args): super().__init__() - self.network = self.build_network(n_in, hidden_dims, n_out) + self.network = self.build_network(n_in, hidden_dims, n_out) # to make predictions about the dynamical field (variable) self.library = Library(library_function, library_args) self.fit = self.build_fit_layer(n_in, n_out, library_function, library_args) def forward(self, input): - prediction = self.network(input) - time_deriv, theta = self.library((prediction, input)) - sparse_theta, coeff_vector = self.fit(theta) + """ + Computes the forward pass of the DeepMoD model. + + Args: + input (torch.Tensor): Input tensor (typically X_train) of shape (batch_size, input_dim). + + Returns: + tuple: A tuple containing: + - prediction (torch.Tensor): Output tensor of shape (batch_size, output_dim). + - time_deriv (torch.Tensor): Time derivative tensor of shape (batch_size, output_dim). + - sparse_theta (torch.Tensor): Sparse theta tensor of shape (n_terms, input_dim). + - coeff_vector (torch.Tensor): Coefficient vector tensor of shape (n_terms, output_dim). + """ + prediction = self.network(input) # predict the fields as a given location (input) + time_deriv, theta = self.library((prediction, input)) # library function returns time_deriv and theta (equation (4) of the manuscript) + sparse_theta, coeff_vector = self.fit(theta) # Note this attribute `fit` of type `Fitting` not a method of NN + # sparse_theta is theta with sparsity mask applied (extracting relevant terms) + # coeff_vector will play role in the loss function (see `losses.py`) which explains how it is optimized return prediction, time_deriv, sparse_theta, coeff_vector def build_network(self, n_in, hidden_dims, n_out): + """ + Builds a neural network with the specified number of input, hidden, and output nodes. + + Args: + n_in (int): Number of input nodes. + hidden_dims (list): List of integers specifying the number of nodes in each hidden layer. + n_out (int): Number of output nodes. + + Returns: + network (nn.Sequential): A PyTorch sequential neural network object. + """ # NN network = [] hs = [n_in] + hidden_dims + [n_out] @@ -30,8 +74,20 @@ def build_network(self, n_in, hidden_dims, n_out): return network def build_fit_layer(self, n_in, n_out, library_function, library_args): + """ + Builds and returns a Fitting layer for the DeepMoD model which is basically the sparse regression layer which applies sparsity mask + + Args: + n_in (int): Number of input features. + n_out (int): Number of output features. + library_function (callable): Function that generates the library. + library_args (dict): Arguments to pass to the library function. + + Returns: + Fitting: A Fitting layer with the appropriate number of terms for the given input and output sizes. + """ sample_input = torch.ones((1, n_in), dtype=torch.float32, requires_grad=True) - n_terms = self.library((self.network(sample_input), sample_input))[1].shape[1] # do sample pass to infer shapes + n_terms = self.library((self.network(sample_input), sample_input))[1].shape[1] # do sample pass to infer shapes: number of terms in the equation fit_layer = Fitting(n_terms, n_out) return fit_layer diff --git a/src/deepymod_torch/library_functions.py b/src/deepymod_torch/library_functions.py index fbae73f..d6ee297 100644 --- a/src/deepymod_torch/library_functions.py +++ b/src/deepymod_torch/library_functions.py @@ -5,7 +5,7 @@ from functools import reduce def library_poly(prediction, max_order): - # Calculate the polynomes of u + # Calculate the polynomials of u (technically these are monomials) u = torch.ones_like(prediction) for order in np.arange(1, max_order+1): u = torch.cat((u, u[:, order-1:order] * prediction), dim=1) @@ -14,28 +14,63 @@ def library_poly(prediction, max_order): def library_deriv(data, prediction, max_order): - dy = grad(prediction, data, grad_outputs=torch.ones_like(prediction), create_graph=True)[0] - time_deriv = dy[:, 0:1] + """ + Computes the time derivative and up to max_order spatial derivatives of the prediction tensor with respect to the data tensor. - if max_order == 0: + Args: + data (torch.Tensor): Input tensor of shape (batch_size, input_dim). Example: X_train. + prediction (torch.Tensor): Output tensor of shape (batch_size, output_dim). Example: y_train_pred. + max_order (int): Maximum order of spatial derivatives to compute. + + Returns: + time_deriv (torch.Tensor): Time derivative of the prediction tensor with respect to the data tensor. + du (torch.Tensor): Tensor of shape (batch_size, max_order+1) containing the computed spatial derivatives. + """ + + dy = grad(prediction, data, grad_outputs=torch.ones_like(prediction), create_graph=True)[0] # Calculate first order derivatives of prediction with respect to data + time_deriv = dy[:, 0:1] # First column is time derivative + + if max_order == 0: # If we only want the time derivative, du is just a scalar du = torch.ones_like(time_deriv) - else: - du = torch.cat((torch.ones_like(time_deriv), dy[:, 1:2]), dim=1) - if max_order >1: + else: # Else we calculate the spatial derivatives + du = torch.cat((torch.ones_like(time_deriv), dy[:, 1:2]), dim=1) # second column of dy gives first order derivative + if max_order > 1: # If we want higher order derivatives, we calculate them successively and concatenate them to du for order in np.arange(1, max_order): du = torch.cat((du, grad(du[:, order:order+1], data, grad_outputs=torch.ones_like(prediction), create_graph=True)[0][:, 1:2]), dim=1) - + return time_deriv, du def library_1D_in(input, poly_order, diff_order): + """ + Computes the library matrix for a spatial 1D input signal, given the input data, the maximum polynomial order and the maximum derivative order. + + Parameters + ---------- + input : tuple of two torch.Tensor + A tuple containing the prediction tensor and the data tensor, both of shape (samples, features). + poly_order : int + The maximum polynomial order to include in the library. + diff_order : int + The maximum derivative order to include in the library. + + Returns + ------- + time_deriv_list : list of torch.Tensor + A list containing the time derivative tensors for each output feature, each of shape (samples, 1). + theta : torch.Tensor + The library matrix, of shape (samples, total_terms), where total_terms is the total number of terms in the library. + when poly_order=2 and diff_order=3 and we have a single output the theta matrix has columns: + ['', 'u_x', 'u_xx', 'u_xxx', 'u', 'uu_x', 'uu_xx', 'uu_xxx', 'u^2', 'u^2u_x', 'u^2u_xx', 'u^2u_xxx'] + For more details run utilities.terms_definition() + """ prediction, data = input poly_list = [] deriv_list = [] time_deriv_list = [] - # Creating lists for all outputs - for output in torch.arange(prediction.shape[1]): + # Creating lists for all outputs + for output in torch.arange(prediction.shape[1]): # loop over all dynamical fields modelled by PDE (in case we have system of PDEs, i.e. more than one dynamical field) time_deriv, du = library_deriv(data, prediction[:, output:output+1], diff_order) u = library_poly(prediction[:, output:output+1], poly_order) @@ -43,15 +78,18 @@ def library_1D_in(input, poly_order, diff_order): deriv_list.append(du) time_deriv_list.append(time_deriv) - samples = time_deriv_list[0].shape[0] - total_terms = poly_list[0].shape[1] * deriv_list[0].shape[1] + samples = time_deriv_list[0].shape[0] # number of samples + total_terms = poly_list[0].shape[1] * deriv_list[0].shape[1] # product of the number of possible polynomials (i.e. monomials) and the number of derivative terms - # Calculating theta - if len(poly_list) == 1: - theta = torch.matmul(poly_list[0][:, :, None], deriv_list[0][:, None, :]).view(samples, total_terms) # If we have a single output, we simply calculate and flatten matrix product between polynomials and derivatives to get library + # Calculating theta matrix (equation (4) of the manuscript) + if len(poly_list) == 1: # If we have a single output (one dynamical field modelled by the PDE), we simply calculate and flatten matrix product between polynomials and derivatives to get library + theta = torch.matmul(poly_list[0][:, :, None], deriv_list[0][:, None, :]).view(samples, total_terms) + # For each sample poly_list[0][each_sample, :] and deriv_list[0][each_sample, :] the above line is equivalent to np.multiply.outer(poly_list[0][each_sample, :],deriv_list[0][each_sample, :] ).reshape(-1) + # so the logic of the expression can be understood by executing np.add.outer(np.array(['', 'u', 'u^2'], object),np.array(['', 'u_x', 'u_xx','u_xxx'], object)).reshape(-1) <- this is consistent with equation (4) + # this means that we iterate over deriv_list first (fast index) and then over poly_list (slow index) + # this gives, for example: ['', 'u_x', 'u_xx', 'u_xxx', 'u', 'uu_x', 'uu_xx', 'uu_xxx', 'u^2', 'u^2u_x', 'u^2u_xx', 'u^2u_xxx'] else: - - theta_uv = reduce((lambda x, y: (x[:, :, None] @ y[:, None, :]).view(samples, -1)), poly_list) + theta_uv = reduce((lambda x, y: (x[:, :, None] @ y[:, None, :]).view(samples, -1)), poly_list) # TODO comment the following lines theta_dudv = torch.cat([torch.matmul(du[:, :, None], dv[:, None, :]).view(samples, -1)[:, 1:] for du, dv in combinations(deriv_list, 2)], 1) # calculate all unique combinations of derivatives theta_udu = torch.cat([torch.matmul(u[:, 1:, None], du[:, None, 1:]).view(samples, (poly_list[0].shape[1]-1) * (deriv_list[0].shape[1]-1)) for u, dv in product(poly_list, deriv_list)], 1) # calculate all unique products of polynomials and derivatives theta = torch.cat([theta_uv, theta_dudv, theta_udu], dim=1) diff --git a/src/deepymod_torch/network.py b/src/deepymod_torch/network.py index 1ad75a3..031d34c 100644 --- a/src/deepymod_torch/network.py +++ b/src/deepymod_torch/network.py @@ -3,6 +3,16 @@ class Library(nn.Module): + """ + A module subclass that represents a library of functions used in the DeepMoD algorithm. + + Args: + library_func (callable): A function that generates the library of functions. + library_args (dict): A dictionary of arguments to be passed to the library function. + + Returns: + tuple: A tuple containing the time derivative list and the theta matrix. + """ def __init__(self, library_func, library_args={}): super().__init__() self.library_func = library_func @@ -14,11 +24,27 @@ def forward(self, input): class Fitting(nn.Module): + """ + A submodule for attaching a sparse regression layer to the DeepMoD model. + + Args: + n_terms (int): The number of terms in the linear combination. + n_out (int): The number of output features. + + Attributes: + coeff_vector (nn.ParameterList): A list of learnable coefficient vectors, one for each output feature. + sparsity_mask (list): A list representing sparsity mask, length corresponds to n_out. + + Methods: + forward(input): Computes the sparse linear combination of input features for each output feature. + apply_mask(theta): Applies the sparsity mask to the input features. + """ def __init__(self, n_terms, n_out): super().__init__() self.coeff_vector = nn.ParameterList([torch.nn.Parameter(torch.rand((n_terms, 1), dtype=torch.float32)) for _ in torch.arange(n_out)]) self.sparsity_mask = [torch.arange(n_terms) for _ in torch.arange(n_out)] - + # sparse_theta is theta with sparsity mask applied (extracting relevant terms) + # coeff_vector will play role in the loss function (see `losses.py`) which explains how it is optimized def forward(self, input): sparse_theta = self.apply_mask(input) return sparse_theta, self.coeff_vector diff --git a/src/deepymod_torch/training.py b/src/deepymod_torch/training.py index f82e7ea..4a55f32 100644 --- a/src/deepymod_torch/training.py +++ b/src/deepymod_torch/training.py @@ -6,7 +6,20 @@ from deepymod_torch.sparsity import scaling, threshold def train(model, data, target, optimizer, max_iterations, loss_func_args={'l1':1e-5}): - '''Trains the deepmod model with MSE, regression and l1 cost function. Updates model in-place.''' + ''' + Trains the deepmod model with MSE, regression and l1 cost function. Updates model in-place. + + Args: + - model: a PyTorch model that implements the deepmod architecture. + - data: a PyTorch tensor containing the input data. Example: X_train. + - target: a PyTorch tensor containing the target data. Example: y_train. + - optimizer: a PyTorch optimizer used for training. + - max_iterations: an integer specifying the maximum number of iterations to train for. + - loss_func_args: a dictionary containing the arguments for the l1 loss function. + + Returns: + - None + ''' start_time = time.time() number_of_terms = [coeff_vec.shape[0] for coeff_vec in model(data)[3]] board = Tensorboard(number_of_terms) @@ -16,12 +29,12 @@ def train(model, data, target, optimizer, max_iterations, loss_func_args={'l1':1 for iteration in torch.arange(0, max_iterations + 1): # Calculating prediction and library and scaling prediction, time_deriv_list, sparse_theta_list, coeff_vector_list = model(data) - coeff_vector_scaled_list = scaling(coeff_vector_list, sparse_theta_list, time_deriv_list) + coeff_vector_scaled_list = scaling(coeff_vector_list, sparse_theta_list, time_deriv_list) # see equation (10) of the manuscript # Calculating loss - loss_reg = reg_loss(time_deriv_list, sparse_theta_list, coeff_vector_list) - loss_mse = mse_loss(prediction, target) - loss_l1 = l1_loss(coeff_vector_scaled_list, loss_func_args['l1']) + loss_reg = reg_loss(time_deriv_list, sparse_theta_list, coeff_vector_list) # equation (7) of the paper + loss_mse = mse_loss(prediction, target) # equation (6) of the paper + loss_l1 = l1_loss(coeff_vector_scaled_list, loss_func_args['l1']) # equation (8) of the paper loss = torch.sum(loss_reg) + torch.sum(loss_mse) + torch.sum(loss_l1) # Writing @@ -64,12 +77,26 @@ def train_mse(model, data, target, optimizer, max_iterations, loss_func_args={}) board.close() def train_deepmod(model, data, target, optimizer, max_iterations, loss_func_args): - '''Performs full deepmod cycle: trains model, thresholds and trains again for unbiased estimate. Updates model in-place.''' + ''' + Performs full deepmod cycle: trains model, thresholds and trains again for unbiased estimate. Updates model in-place. + + Args: + model (torch.nn.Module): The PyTorch model to be trained. + data (torch.Tensor): The input data tensor. + target (torch.Tensor): The target data tensor. + optimizer (torch.optim.Optimizer): The optimizer to use for training. + max_iterations (int): The maximum number of iterations to train for. + loss_func_args (dict): A dictionary of additional arguments to pass to the loss function. + + Returns: + None + ''' # Train first cycle and get prediction train(model, data, target, optimizer, max_iterations, loss_func_args) prediction, time_deriv_list, sparse_theta_list, coeff_vector_list = model(data) # Threshold, set sparsity mask and coeff vector + # The supplementary material of the paper explains: "After the total loss of the neural network has converged we threshold the resulting weight vector and obtain the" sparse_coeff_vector_list, sparsity_mask_list = threshold(coeff_vector_list, sparse_theta_list, time_deriv_list) model.fit.sparsity_mask = sparsity_mask_list model.fit.coeff_vector = torch.nn.ParameterList(sparse_coeff_vector_list)