From 6c99c6b7eb2347570eaf18c245eece147e4b67c8 Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 4 Mar 2025 14:31:39 -0500 Subject: [PATCH] Created using Colab --- .../Chap17/17_3_Importance_Sampling.ipynb | 34 ++++++------------- 1 file changed, 11 insertions(+), 23 deletions(-) diff --git a/Notebooks/Chap17/17_3_Importance_Sampling.ipynb b/Notebooks/Chap17/17_3_Importance_Sampling.ipynb index da920b4..402460f 100644 --- a/Notebooks/Chap17/17_3_Importance_Sampling.ipynb +++ b/Notebooks/Chap17/17_3_Importance_Sampling.ipynb @@ -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": [ "\"Open" ] }, { - "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 -} +} \ No newline at end of file