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search algos
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‎README.md

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@@ -34,6 +34,10 @@ If you have any suggestions or want to make additions, I would be very happy if
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- Queues and Deques [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/data-structures/queues-and-deques.ipynb)]
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### Search Algorithms
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- Binary Search [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/search/binary_search.ipynb)]
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### Statistical Analysis
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- Linear regression via the least squares fit method [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/linregr_least_squares_fit.ipynb)]

‎ipython_nbs/search/binary_search.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sebastian Raschka \n",
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"last updated: 2017-07-25 \n",
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"\n",
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"CPython 3.6.1\n",
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"IPython 6.0.0\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -a 'Sebastian Raschka' -u -d -v"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Binary Search"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"An implementation of the binary search algorithm. For details will follow. A good summary can be found on Wikipedia: https://en.wikipedia.org/wiki/Binary_search_algorithm."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The figures below provide a short illustration of how the implementation works on a toy example:\n",
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"\n",
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"![](images/binary_search/ex-1-1.png)\n",
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"\n",
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"![](images/binary_search/ex-1-2.png)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def binary_search(array, value):\n",
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" ary = array\n",
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" min_idx = 0\n",
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" max_idx = len(array)\n",
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" \n",
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" while min_idx < max_idx:\n",
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" middle_idx = min_idx + (max_idx - min_idx) // 2\n",
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"\n",
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" if array[middle_idx] == value:\n",
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" return middle_idx\n",
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" elif array[middle_idx] < value:\n",
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" min_idx = middle_idx + 1\n",
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" else:\n",
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" max_idx = middle_idx\n",
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" \n",
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" return None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"binary_search(array=[1, 2, 4, 7, 8, 10, 11],\n",
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" value=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"binary_search(array=[1, 2, 4, 7, 8, 10, 11],\n",
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" value=2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"binary_search(array=[1, 2, 4, 7, 8, 10, 11],\n",
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" value=4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"6"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"binary_search(array=[1, 2, 4, 7, 8, 10, 11],\n",
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" value=11)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"binary_search(array=[1, 2, 4, 7, 8, 10, 11],\n",
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" value=99)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎ipython_nbs/search/images/binary_search/ex-1-1.png

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‎ipython_nbs/search/images/binary_search/ex-1-2.png

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