Automatize Tutorials html, py files creation (#496)
* workflow to export tutorials ---------
This commit is contained in:
Dario Coscia
committed by
FilippoOlivo
FilippoOlivo
parent
8dfc9d19db
commit
3ff9f0c9a2
188
tutorials/tutorial14/tutorial.ipynb
vendored
188
tutorials/tutorial14/tutorial.ipynb
vendored
@@ -33,12 +33,13 @@
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"source": [
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"## routine needed to run the notebook on Google Colab\n",
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"try:\n",
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" import google.colab\n",
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" IN_COLAB = True\n",
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" import google.colab\n",
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"\n",
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" IN_COLAB = True\n",
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"except:\n",
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" IN_COLAB = False\n",
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" IN_COLAB = False\n",
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"if IN_COLAB:\n",
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" !pip install \"pina-mathlab\"\n",
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" !pip install \"pina-mathlab\"\n",
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"\n",
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"%matplotlib inline\n",
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"\n",
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@@ -50,7 +51,7 @@
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"from pina.model.block import PODBlock, RBFBlock\n",
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"from pina import LabelTensor\n",
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"\n",
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"warnings.filterwarnings('ignore')"
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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@@ -70,6 +71,7 @@
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"source": [
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"import smithers\n",
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"from smithers.dataset import LidCavity\n",
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"\n",
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"dataset = LidCavity()"
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]
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},
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@@ -108,13 +110,13 @@
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],
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"source": [
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"fig, axs = plt.subplots(1, 3, figsize=(14, 3))\n",
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"for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots['mag(v)'][:3]):\n",
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"for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots[\"mag(v)\"][:3]):\n",
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" ax.tricontourf(dataset.triang, u, levels=16)\n",
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" ax.set_title(f'$u$ field for $\\mu$ = {par[0]:.4f}')\n",
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" ax.set_title(f\"$u$ field for $\\mu$ = {par[0]:.4f}\")\n",
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"fig, axs = plt.subplots(1, 3, figsize=(14, 3))\n",
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"for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots['p'][:3]):\n",
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"for ax, par, u in zip(axs, dataset.params[:3], dataset.snapshots[\"p\"][:3]):\n",
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" ax.tricontourf(dataset.triang, u, levels=16)\n",
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" ax.set_title(f'$p$ field for $\\mu$ = {par[0]:.4f}')"
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" ax.set_title(f\"$p$ field for $\\mu$ = {par[0]:.4f}\")"
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]
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},
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{
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@@ -130,15 +132,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"'''velocity magnitude data, 5041 for each snapshot'''\n",
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"u=torch.tensor(dataset.snapshots['mag(v)']).float() \n",
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"u = LabelTensor(u, labels=[f's{i}' for i in range(u.shape[1])])\n",
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"'''pressure data, 5041 for each snapshot'''\n",
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"p=torch.tensor(dataset.snapshots['p']).float()\n",
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"p = LabelTensor(p, labels=[f's{i}' for i in range(p.shape[1])])\n",
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"'''mu corresponding to each snapshot'''\n",
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"mu=torch.tensor(dataset.params).float()\n",
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"mu = LabelTensor(mu, labels=['mu'])\n"
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"\"\"\"velocity magnitude data, 5041 for each snapshot\"\"\"\n",
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"\n",
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"u = torch.tensor(dataset.snapshots[\"mag(v)\"]).float()\n",
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"u = LabelTensor(u, labels=[f\"s{i}\" for i in range(u.shape[1])])\n",
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"\"\"\"pressure data, 5041 for each snapshot\"\"\"\n",
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"p = torch.tensor(dataset.snapshots[\"p\"]).float()\n",
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"p = LabelTensor(p, labels=[f\"s{i}\" for i in range(p.shape[1])])\n",
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"\"\"\"mu corresponding to each snapshot\"\"\"\n",
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"mu = torch.tensor(dataset.params).float()\n",
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"mu = LabelTensor(mu, labels=[\"mu\"])"
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]
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},
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{
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@@ -154,15 +157,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"'''number of snapshots'''\n",
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"\"\"\"number of snapshots\"\"\"\n",
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"\n",
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"n = u.shape[0]\n",
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"'''training over total snapshots ratio and number of training snapshots'''\n",
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"ratio = 0.9 \n",
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"n_train = int(n*ratio)\n",
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"'''split u and p data'''\n",
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"u_train, u_test = u[:n_train], u[n_train:] #for mag(v)\n",
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"p_train, p_test = p[:n_train], p[n_train:] #for p\n",
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"'''split snapshots'''\n",
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"\"\"\"training over total snapshots ratio and number of training snapshots\"\"\"\n",
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"ratio = 0.9\n",
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"n_train = int(n * ratio)\n",
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"\"\"\"split u and p data\"\"\"\n",
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"u_train, u_test = u[:n_train], u[n_train:] # for mag(v)\n",
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"p_train, p_test = p[:n_train], p[n_train:] # for p\n",
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"\"\"\"split snapshots\"\"\"\n",
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"mu_train, mu_test = mu[:n_train], mu[n_train:]"
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]
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},
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@@ -183,8 +187,9 @@
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" \"\"\"\n",
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" Proper orthogonal decomposition with Radial Basis Function interpolation model.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, pod_rank, rbf_kernel):\n",
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" \n",
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"\n",
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" super().__init__()\n",
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" self.pod = PODBlock(pod_rank)\n",
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" self.rbf = RBFBlock(kernel=rbf_kernel)"
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@@ -207,8 +212,9 @@
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" \"\"\"\n",
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" Proper orthogonal decomposition with Radial Basis Function interpolation model.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, pod_rank, rbf_kernel):\n",
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" \n",
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"\n",
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" super().__init__()\n",
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" self.pod = PODBlock(pod_rank)\n",
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" self.rbf = RBFBlock(kernel=rbf_kernel)\n",
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@@ -223,6 +229,7 @@
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" \"\"\"\n",
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" coefficients = self.rbf(x)\n",
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" return self.pod.expand(coefficients)\n",
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"\n",
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" def fit(self, p, x):\n",
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" \"\"\"\n",
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" Call the :meth:`pina.model.layers.PODBlock.fit` method of the\n",
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@@ -231,8 +238,7 @@
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" :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation.\n",
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" \"\"\"\n",
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" self.pod.fit(x)\n",
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" self.rbf.fit(p, self.pod.reduce(x))\n",
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" "
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" self.rbf.fit(p, self.pod.reduce(x))"
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]
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},
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{
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@@ -248,15 +254,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"'''create the model'''\n",
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"pod_rbfu = PODRBF(pod_rank=20, rbf_kernel='thin_plate_spline')\n",
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"\"\"\"create the model\"\"\"\n",
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"\n",
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"'''fit the model to velocity training data'''\n",
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"pod_rbfu = PODRBF(pod_rank=20, rbf_kernel=\"thin_plate_spline\")\n",
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"\n",
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"\"\"\"fit the model to velocity training data\"\"\"\n",
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"pod_rbfu.fit(mu_train, u_train)\n",
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"\n",
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"'''predict the parameter using the fitted model'''\n",
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"\"\"\"predict the parameter using the fitted model\"\"\"\n",
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"u_train_rbf = pod_rbfu(mu_train)\n",
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"u_test_rbf = pod_rbfu(mu_test)\n"
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"u_test_rbf = pod_rbfu(mu_test)"
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]
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},
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{
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@@ -282,12 +289,12 @@
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}
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],
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"source": [
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"relative_u_error_train = torch.norm(u_train_rbf - u_train)/torch.norm(u_train)\n",
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"relative_u_error_test = torch.norm(u_test_rbf - u_test)/torch.norm(u_test)\n",
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"relative_u_error_train = torch.norm(u_train_rbf - u_train) / torch.norm(u_train)\n",
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"relative_u_error_test = torch.norm(u_test_rbf - u_test) / torch.norm(u_test)\n",
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"\n",
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"print('Error summary for POD-RBF model:')\n",
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"print(f' Train: {relative_u_error_train.item():e}')\n",
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"print(f' Test: {relative_u_error_test.item():e}')"
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"print(\"Error summary for POD-RBF model:\")\n",
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"print(f\" Train: {relative_u_error_train.item():e}\")\n",
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"print(f\" Test: {relative_u_error_test.item():e}\")"
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]
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},
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{
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@@ -323,23 +330,32 @@
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"fig, axs = plt.subplots(3, 4, figsize=(14, 10))\n",
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"\n",
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"relative_u_error_rbf = np.abs(u_test[idx] - u_idx_rbf.detach())\n",
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"relative_u_error_rbf = np.where(u_test[idx] < 1e-7, 1e-7, relative_u_error_rbf/u_test[idx])\n",
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" \n",
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"for i, (idx_, rbf_, rbf_err_) in enumerate(\n",
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" zip(idx, u_idx_rbf, relative_u_error_rbf)):\n",
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" axs[0, i].set_title('Prediction for ' f'$\\mu$ = {mu_test[idx_].item():.4f}')\n",
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" axs[1, i].set_title('True snapshot for ' f'$\\mu$ = {mu_test[idx_].item():.4f}')\n",
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" axs[2, i].set_title('Error for ' f'$\\mu$ = {mu_test[idx_].item():.4f}')\n",
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"relative_u_error_rbf = np.where(\n",
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" u_test[idx] < 1e-7, 1e-7, relative_u_error_rbf / u_test[idx]\n",
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")\n",
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"\n",
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" cm = axs[0, i].tricontourf(dataset.triang, rbf_.detach()) # POD-RBF prediction\n",
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"for i, (idx_, rbf_, rbf_err_) in enumerate(\n",
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" zip(idx, u_idx_rbf, relative_u_error_rbf)\n",
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"):\n",
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" axs[0, i].set_title(\"Prediction for \" f\"$\\mu$ = {mu_test[idx_].item():.4f}\")\n",
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" axs[1, i].set_title(\n",
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" \"True snapshot for \" f\"$\\mu$ = {mu_test[idx_].item():.4f}\"\n",
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" )\n",
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" axs[2, i].set_title(\"Error for \" f\"$\\mu$ = {mu_test[idx_].item():.4f}\")\n",
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"\n",
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" cm = axs[0, i].tricontourf(\n",
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" dataset.triang, rbf_.detach()\n",
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" ) # POD-RBF prediction\n",
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" plt.colorbar(cm, ax=axs[0, i])\n",
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" \n",
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" cm = axs[1, i].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth\n",
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"\n",
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" cm = axs[1, i].tricontourf(dataset.triang, u_test[idx_].flatten()) # Truth\n",
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" plt.colorbar(cm, ax=axs[1, i])\n",
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"\n",
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" cm = axs[2, i].tripcolor(dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()) # Error for POD-RBF\n",
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" cm = axs[2, i].tripcolor(\n",
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" dataset.triang, rbf_err_, norm=matplotlib.colors.LogNorm()\n",
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" ) # Error for POD-RBF\n",
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" plt.colorbar(cm, ax=axs[2, i])\n",
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" \n",
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"\n",
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"plt.show()"
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]
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},
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@@ -366,22 +382,23 @@
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}
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],
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"source": [
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"'''create the model'''\n",
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"pod_rbfp = PODRBF(pod_rank=20, rbf_kernel='thin_plate_spline')\n",
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"\"\"\"create the model\"\"\"\n",
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"\n",
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"'''fit the model to pressure training data'''\n",
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"pod_rbfp = PODRBF(pod_rank=20, rbf_kernel=\"thin_plate_spline\")\n",
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"\n",
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"\"\"\"fit the model to pressure training data\"\"\"\n",
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"pod_rbfp.fit(mu_train, p_train)\n",
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"\n",
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"'''predict the parameter using the fitted model'''\n",
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"\"\"\"predict the parameter using the fitted model\"\"\"\n",
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"p_train_rbf = pod_rbfp(mu_train)\n",
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"p_test_rbf = pod_rbfp(mu_test)\n",
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"\n",
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"relative_p_error_train = torch.norm(p_train_rbf - p_train)/torch.norm(p_train)\n",
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"relative_p_error_test = torch.norm(p_test_rbf - p_test)/torch.norm(p_test)\n",
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"relative_p_error_train = torch.norm(p_train_rbf - p_train) / torch.norm(p_train)\n",
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"relative_p_error_test = torch.norm(p_test_rbf - p_test) / torch.norm(p_test)\n",
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"\n",
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"print('Error summary for POD-RBF model:')\n",
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"print(f' Train: {relative_p_error_train.item():e}')\n",
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"print(f' Test: {relative_p_error_test.item():e}')"
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"print(\"Error summary for POD-RBF model:\")\n",
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"print(f\" Train: {relative_p_error_train.item():e}\")\n",
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"print(f\" Test: {relative_p_error_test.item():e}\")"
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]
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},
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{
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@@ -409,10 +426,12 @@
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],
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"source": [
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"fig, axs = plt.subplots(2, 3, figsize=(14, 6))\n",
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"for ax, par, u in zip(axs.ravel(), dataset.params[66:72], dataset.snapshots['p'][66:72]):\n",
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"for ax, par, u in zip(\n",
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" axs.ravel(), dataset.params[66:72], dataset.snapshots[\"p\"][66:72]\n",
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"):\n",
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" cm = ax.tricontourf(dataset.triang, u, levels=16)\n",
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" plt.colorbar(cm, ax=ax)\n",
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" ax.set_title(f'$p$ field for $\\mu$ = {par[0]:.4f}')\n",
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" ax.set_title(f\"$p$ field for $\\mu$ = {par[0]:.4f}\")\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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@@ -442,11 +461,13 @@
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],
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"source": [
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"fig, axs = plt.subplots(2, 3, figsize=(14, 6))\n",
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"for ax, par, u in zip(axs.ravel(), dataset.params[98:104], dataset.snapshots['p'][98:104]):\n",
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"for ax, par, u in zip(\n",
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" axs.ravel(), dataset.params[98:104], dataset.snapshots[\"p\"][98:104]\n",
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"):\n",
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" cm = ax.tricontourf(dataset.triang, u, levels=16)\n",
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" plt.colorbar(cm, ax=ax)\n",
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" ax.set_title(f'$p$ field for $\\mu$ = {par[0]:.4f}')\n",
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"plt.tight_layout() \n",
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" ax.set_title(f\"$p$ field for $\\mu$ = {par[0]:.4f}\")\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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@@ -473,37 +494,42 @@
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}
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],
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"source": [
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"'''excluding problematic snapshots'''\n",
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"\"\"\"excluding problematic snapshots\"\"\"\n",
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"\n",
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"data = list(range(300))\n",
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"data_to_consider = data[:67] + data[71:100] + data[102:]\n",
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"'''proceed as before'''\n",
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"newp=torch.tensor(dataset.snapshots['p'][data_to_consider]).float()\n",
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"newp = LabelTensor(newp, labels=[f's{i}' for i in range(newp.shape[1])])\n",
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"\"\"\"proceed as before\"\"\"\n",
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"newp = torch.tensor(dataset.snapshots[\"p\"][data_to_consider]).float()\n",
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"newp = LabelTensor(newp, labels=[f\"s{i}\" for i in range(newp.shape[1])])\n",
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"\n",
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"newmu=torch.tensor(dataset.params[data_to_consider]).float()\n",
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"newmu = LabelTensor(newmu, labels=['mu'])\n",
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"newmu = torch.tensor(dataset.params[data_to_consider]).float()\n",
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"newmu = LabelTensor(newmu, labels=[\"mu\"])\n",
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"\n",
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"newn = newp.shape[0]\n",
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"ratio = 0.9 \n",
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"new_train = int(newn*ratio)\n",
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"ratio = 0.9\n",
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"new_train = int(newn * ratio)\n",
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"\n",
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"new_p_train, new_p_test = newp[:new_train], newp[new_train:] \n",
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"new_p_train, new_p_test = newp[:new_train], newp[new_train:]\n",
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"\n",
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"new_mu_train, new_mu_test = newmu[:new_train], newmu[new_train:]\n",
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"\n",
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"new_pod_rbfp = PODRBF(pod_rank=20, rbf_kernel='thin_plate_spline')\n",
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"new_pod_rbfp = PODRBF(pod_rank=20, rbf_kernel=\"thin_plate_spline\")\n",
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"\n",
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"new_pod_rbfp.fit(new_mu_train, new_p_train)\n",
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"\n",
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"new_p_train_rbf = new_pod_rbfp(new_mu_train)\n",
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"new_p_test_rbf = new_pod_rbfp(new_mu_test)\n",
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"\n",
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"new_relative_p_error_train = torch.norm(new_p_train_rbf - new_p_train)/torch.norm(new_p_train)\n",
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"new_relative_p_error_test = torch.norm(new_p_test_rbf - new_p_test)/torch.norm(new_p_test)\n",
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"new_relative_p_error_train = torch.norm(\n",
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" new_p_train_rbf - new_p_train\n",
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") / torch.norm(new_p_train)\n",
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"new_relative_p_error_test = torch.norm(\n",
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" new_p_test_rbf - new_p_test\n",
|
||||
") / torch.norm(new_p_test)\n",
|
||||
"\n",
|
||||
"print('Error summary for POD-RBF model:')\n",
|
||||
"print(f' Train: {new_relative_p_error_train.item():e}')\n",
|
||||
"print(f' Test: {new_relative_p_error_test.item():e}')"
|
||||
"print(\"Error summary for POD-RBF model:\")\n",
|
||||
"print(f\" Train: {new_relative_p_error_train.item():e}\")\n",
|
||||
"print(f\" Test: {new_relative_p_error_test.item():e}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user