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@@ -3,8 +3,8 @@
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {
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"metadata": {
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"colab_type": "text",
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"id": "view-in-github",
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"id": "view-in-github"
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"colab_type": "text"
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},
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},
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"source": [
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"source": [
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"<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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@@ -405,11 +405,11 @@
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"\n",
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"\n",
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" # TODO Write this function\n",
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" # TODO Write this function\n",
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" # 1. For each x (value in x_plot_vals):\n",
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" # 1. For each x (value in x_plot_vals):\n",
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" # 2. Compute the mean and variance of the diffusion kernel at time t\n",
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" # 2. Compute the mean and variance of the diffusion kernel at time t\n",
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" # 3. Compute pdf of this Gaussian at every x_plot_val\n",
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" # 3. Compute pdf of this Gaussian at every x_plot_val\n",
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" # 4. Weight Gaussian by probability at position x and by 0.01 to componensate for bin size\n",
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" # 4. Weight Gaussian by probability at position x and by 0.01 to componensate for bin size\n",
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" # 5. Accumulate weighted Gaussian in marginal at time t.\n",
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" # 5. Accumulate weighted Gaussian in marginal at time t.\n",
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" # 6. Multiply result by 0.01 to compensate for bin size\n",
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"\n",
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" # Replace this line:\n",
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" # Replace this line:\n",
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" marginal_at_time_t = marginal_at_time_t\n",
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" marginal_at_time_t = marginal_at_time_t\n",
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"\n",
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"\n",
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@@ -454,9 +454,8 @@
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],
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],
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"metadata": {
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"metadata": {
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"colab": {
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"colab": {
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"authorship_tag": "ABX9TyMpC8kgLnXx0XQBtwNAQ4jJ",
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"provenance": [],
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"include_colab_link": true,
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"include_colab_link": true
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"provenance": []
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},
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},
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3",
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