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udlbook
2025-03-04 14:31:39 -05:00
parent 0988ae8bd0
commit 6c99c6b7eb
1 changed files with 11 additions and 23 deletions
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32
Notebooks/Chap17/17_3_Importance_Sampling.ipynb
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@@ -1,18 +1,16 @@
{ {
"cells": [ "cells": [
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"colab_type": "text", "id": "view-in-github",
"id": "view-in-github" "colab_type": "text"
}, },
"source": [ "source": [
"<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "t9vk9Elugvmi" "id": "t9vk9Elugvmi"
@@ -40,7 +38,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "f7a6xqKjkmvT" "id": "f7a6xqKjkmvT"
@@ -126,7 +123,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "Jr4UPcqmnXCS" "id": "Jr4UPcqmnXCS"
@@ -166,8 +162,8 @@
"mean_all = np.zeros_like(n_sample_all)\n", "mean_all = np.zeros_like(n_sample_all)\n",
"variance_all = np.zeros_like(n_sample_all)\n", "variance_all = np.zeros_like(n_sample_all)\n",
"for i in range(len(n_sample_all)):\n", "for i in range(len(n_sample_all)):\n",
" print(\"Computing mean and variance for expectation with %d samples\"%(n_sample_all[i]))\n", " mean_all[i],variance_all[i] = compute_mean_variance(n_sample_all[i])\n",
" mean_all[i],variance_all[i] = compute_mean_variance(n_sample_all[i])" " print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all[i], \", Variance: \", variance_all[i])"
] ]
}, },
{ {
@@ -189,7 +185,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "XTUpxFlSuOl7" "id": "XTUpxFlSuOl7"
@@ -199,7 +194,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "6hxsl3Pxo1TT" "id": "6hxsl3Pxo1TT"
@@ -234,7 +228,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "G9Xxo0OJsIqD" "id": "G9Xxo0OJsIqD"
@@ -283,7 +276,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "2sVDqP0BvxqM" "id": "2sVDqP0BvxqM"
@@ -313,8 +305,8 @@
"mean_all2 = np.zeros_like(n_sample_all)\n", "mean_all2 = np.zeros_like(n_sample_all)\n",
"variance_all2 = np.zeros_like(n_sample_all)\n", "variance_all2 = np.zeros_like(n_sample_all)\n",
"for i in range(len(n_sample_all)):\n", "for i in range(len(n_sample_all)):\n",
" print(\"Computing variance for expectation with %d samples\"%(n_sample_all[i]))\n", " mean_all2[i], variance_all2[i] = compute_mean_variance2(n_sample_all[i])\n",
" mean_all2[i], variance_all2[i] = compute_mean_variance2(n_sample_all[i])" " print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all2[i], \", Variance: \", variance_all2[i])"
] ]
}, },
{ {
@@ -348,7 +340,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "EtBP6NeLwZqz" "id": "EtBP6NeLwZqz"
@@ -360,7 +351,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "_wuF-NoQu1--" "id": "_wuF-NoQu1--"
@@ -432,8 +422,8 @@
"mean_all2b = np.zeros_like(n_sample_all)\n", "mean_all2b = np.zeros_like(n_sample_all)\n",
"variance_all2b = np.zeros_like(n_sample_all)\n", "variance_all2b = np.zeros_like(n_sample_all)\n",
"for i in range(len(n_sample_all)):\n", "for i in range(len(n_sample_all)):\n",
" print(\"Computing variance for expectation with %d samples\"%(n_sample_all[i]))\n", " mean_all2b[i], variance_all2b[i] = compute_mean_variance2b(n_sample_all[i])\n",
" mean_all2b[i], variance_all2b[i] = compute_mean_variance2b(n_sample_all[i])" " print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all2b[i], \", Variance: \", variance_all2b[i])"
] ]
}, },
{ {
@@ -478,7 +468,6 @@
] ]
}, },
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "y8rgge9MNiOc" "id": "y8rgge9MNiOc"
@@ -490,9 +479,8 @@
], ],
"metadata": { "metadata": {
"colab": { "colab": {
"authorship_tag": "ABX9TyNecz9/CDOggPSmy1LjT/Dv", "provenance": [],
"include_colab_link": true, "include_colab_link": true
"provenance": []
}, },
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",