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snailkn
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‎cha2/cha2_1.ipynb‎

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{
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"cells": [
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{
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"cell_type": "heading",
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"metadata": {
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"collapsed": true
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},
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"level": 1,
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"source": [
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"算法基础 - 插入排序"
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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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"> 1.插入法降序排序"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import random\n",
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"import time"
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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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"collapsed": true
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},
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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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"[26247 27748 31404 ..., 16477 412 25203]\n"
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]
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},
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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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"[49997 49993 49991 ..., 12 9 2]\n15.146586179733276\n"
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]
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}
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],
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"source": [
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"def insertion_sort(array):\n",
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" length = len(array)\n",
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" for i in range(1, length):\n",
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" key = array[i]\n",
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" j = i-1\n",
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" while j >= 0 and array[j] < key:\n",
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" array[j+1] = array[j]\n",
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" j -= 1\n",
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" array[j+1] = key\n",
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" return array\n",
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"\n",
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"a = np.array(random.sample(range(50000), 10000))\n",
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"print(a)\n",
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"t = time.time()\n",
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"print(insertion_sort(a))\n",
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"print(time.time()-t)"
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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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"> 2.使用线性查找确认给定数字是否在给定数组中，返回对应的数组下标或者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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 1 2 3 4 5 6 7 8 9]\n[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, None]\n"
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]
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}
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],
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"source": [
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"def find_num(array, num):\n",
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" length = len(array)\n",
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" i = 0\n",
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" while i < length and array[i] != num:\n",
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" i += 1\n",
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" return i if i < length else None\n",
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"\n",
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"a = np.array(range(10))\n",
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"print(a)\n",
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"print([find_num(a, x) for x in range(11)])"
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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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"> 3.用n元数组模拟两个n位二进制数相加的过程"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 0 1 1]\n"
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]
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}
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],
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"source": [
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"def badd(a, b):\n",
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" if len(a) != len(b) or (set(a + b) != {0, 1} and set(a + b) != {1}):\n",
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" print('input error')\n",
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" n = len(a)\n",
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" i = temp = 0\n",
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" c = np.array([0] * (n + 1))\n",
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" while i < n:\n",
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" sum = a[i] + b[i] + temp\n",
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" c[i], temp = sum % 2, sum // 2\n",
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" i += 1\n",
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" c[n] = temp\n",
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" return c\n",
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"print(badd([1, 1, 1], [1, 0, 1]))\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 2",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎cha2/cha2_2.ipynb‎

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{
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"cells": [
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{
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"cell_type": "heading",
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"metadata": {
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"collapsed": true
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},
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"level": 1,
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"source": [
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"算法基础 - 分析算法"
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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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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import random\n",
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"import time"
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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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"> 2.2选择算法对数组进行排序"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[13587 28547 10141 ..., 31846 32712 7809]\n"
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]
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},
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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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"[ 0 1 19 ..., 49990 49992 49997]\n10.699007987976074\n"
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]
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}
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],
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"source": [
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"def selection_sort(array):\n",
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" length = len(array)\n",
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" m, min = 0, array[0]\n",
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" for i in range(1, length):\n",
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" for j in range(i, length):\n",
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" if array[j] < min:\n",
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" m, min = j, array[j]\n",
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" j += 1\n",
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" array[m] = array[i-1]\n",
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" array[i-1] = min\n",
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" m, min = i, array[i]\n",
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" i += 1\n",
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" return array\n",
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"\n",
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"a = np.array(random.sample(range(50000), 10000)) \n",
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"print(a)\n",
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"t = time.time()\n",
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"print(selection_sort(a))\n",
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"print(time.time()-t)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 2",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎cha2/cha2_3.ipynb‎

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{
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"cells": [
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{
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"cell_type": "heading",
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"metadata": {
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"collapsed": true
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},
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"level": 1,
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"source": [
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"算法基础 - 设计算法"
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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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"> 归并排序"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import random\n",
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"import time"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 3875 45858 22 ..., 19760 20885 36795]\n[ 3.00000000e+00 1.50000000e+01 1.60000000e+01 ..., 4.99860000e+04\n 4.99950000e+04 4.99980000e+04]\n0.14264893531799316\n"
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]
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}
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],
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"source": [
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"def merge(a1, a2):\n",
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" l1, l2 = len(a1), len(a2)\n",
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" l = l1 + l2\n",
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" ar = np.empty(l)\n",
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" i = j = r = 0\n",
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" while i != l1 and j != l2:\n",
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" if a1[i] <= a2[j]:\n",
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" ar[r] = a1[i]\n",
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" i += 1\n",
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" else:\n",
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" ar[r] = a2[j]\n",
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" j += 1\n",
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" r += 1\n",
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" if i == l1:\n",
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" ar[r:] = a2[j:]\n",
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" else:\n",
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" ar[r:] = a1[i:]\n",
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" return ar\n",
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" \n",
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"def merge_sort(array):\n",
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" length = len(array)\n",
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" if length == 1:\n",
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" return array\n",
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" p = length//2\n",
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" a1 = merge_sort(array[0: p])\n",
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" a2 = merge_sort(array[p: length])\n",
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" return merge(a1, a2)\n",
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"\n",
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"a = np.array(random.sample(range(50000), 10000))\n",
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"print(a)\n",
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"t = time.time()\n",
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"print(merge_sort(a))\n",
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"print(time.time()-t)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 2",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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