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Created using Colab

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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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34
Notebooks/Chap17/17_3_Importance_Sampling.ipynb
View File

@@ -1,18 +1,16 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
"id": "view-in-github",
"colab_type": "text"
},
"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>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "t9vk9Elugvmi"
@@ -40,7 +38,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "f7a6xqKjkmvT"
@@ -126,7 +123,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "Jr4UPcqmnXCS"
@@ -166,8 +162,8 @@
"mean_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",
" 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])"
" mean_all[i],variance_all[i] = compute_mean_variance(n_sample_all[i])\n",
" print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all[i], \", Variance: \", variance_all[i])"
]
},
{
@@ -189,7 +185,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "XTUpxFlSuOl7"
@@ -199,7 +194,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "6hxsl3Pxo1TT"
@@ -234,7 +228,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "G9Xxo0OJsIqD"
@@ -283,7 +276,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "2sVDqP0BvxqM"
@@ -313,8 +305,8 @@
"mean_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",
" 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])"
" mean_all2[i], variance_all2[i] = compute_mean_variance2(n_sample_all[i])\n",
" print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all2[i], \", Variance: \", variance_all2[i])"
]
},
{
@@ -348,7 +340,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "EtBP6NeLwZqz"
@@ -360,7 +351,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "_wuF-NoQu1--"
@@ -432,8 +422,8 @@
"mean_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",
" 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])"
" mean_all2b[i], variance_all2b[i] = compute_mean_variance2b(n_sample_all[i])\n",
" print(\"No samples: \", n_sample_all[i], \", Mean: \", mean_all2b[i], \", Variance: \", variance_all2b[i])"
]
},
{
@@ -478,7 +468,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "y8rgge9MNiOc"
@@ -490,9 +479,8 @@
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyNecz9/CDOggPSmy1LjT/Dv",
"include_colab_link": true,
"provenance": []
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
@@ -504,4 +492,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}