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Samuel FlenderSamuel Flender
Samuel Flender
authored and
Samuel Flender
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update all lessons
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‎Lesson 1 - Linked Lists.ipynb renamed to ‎Lesson 01 - Linked Lists.ipynb

File renamed without changes.

‎Lesson 2 - Binary Trees.ipynb renamed to ‎Lesson 02 - Binary Trees.ipynb

Lines changed: 131 additions & 8 deletions
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@@ -20,7 +20,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -51,19 +51,16 @@
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" if not node:\n",
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" return []\n",
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" return dfs_inorder(node.l) + [node.v] + dfs_inorder(node.r)\n",
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" return vals\n",
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"\n",
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"def dfs_preorder(node):\n",
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" if not node:\n",
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" return []\n",
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" return [node.v] + dfs_inorder(node.l) + dfs_inorder(node.r)\n",
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" return vals\n",
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"\n",
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"def dfs_postorder(node):\n",
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" if not node:\n",
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" return []\n",
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" return dfs_inorder(node.l) + dfs_inorder(node.r) + [node.v]\n",
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" return vals\n",
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"\n",
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"def dfs_iterative(node):\n",
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" # surprise! we can also implement DFS iteratively, by replacing the Q with a stack:\n",
@@ -81,7 +78,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -101,7 +98,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
@@ -176,12 +173,138 @@
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"dfs_inorder(M)"
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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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"## Is same-tree and is sub-tree problems"
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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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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"def same(n,m):\n",
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" '''\n",
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" returns True if n and m are the same tree\n",
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" '''\n",
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" \n",
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" if not n and not m: \n",
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" return True\n",
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" if (n and not m) or (m and not n): \n",
197+
" return False\n",
198+
" if n.val!=m.val:\n",
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" return False\n",
200+
" return same(n.l,m.l) and same(n.r,m.r)"
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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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"source": [
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"def sub(n,m):\n",
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" '''\n",
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" returns True if m is a subtree of n\n",
212+
" '''\n",
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" \n",
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" if same(n,m): return True\n",
215+
" if not m: return True\n",
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" if not n: return False\n",
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" return sub(n.l,m) or sub(n.r,m) "
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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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"## Problem: find lowest common ancesctor in BST"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"def lca(root,n,m):\n",
234+
" if n.v>root.v and m.v>root.v:\n",
235+
" # both are bigger, go to the right\n",
236+
" return lca(root.r,n,m)\n",
237+
"\n",
238+
" elif n.v<root.v and m.v<root.v:\n",
239+
" # both are smaller, go to the left\n",
240+
" return lca(root.l,n,m)\n",
241+
"\n",
242+
" else:\n",
243+
" # one is bigger, one is smaller - bingo!\n",
244+
" return root.v"
245+
]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Consider this BST:\n",
254+
"# 5\n",
255+
"# 3 7\n",
256+
"# 2 4 6 8\n",
257+
"#\n",
258+
"# traversing this \"in order\" --> 2 3 4 5 6 7 8\n",
259+
"\n",
260+
"M = Node(5)\n",
261+
"M.l = Node(3)\n",
262+
"M.r = Node(7)\n",
263+
"M.l.l = Node(2)\n",
264+
"M.l.r = Node(4)\n",
265+
"M.r.l = Node(6)\n",
266+
"M.r.r = Node(8)"
267+
]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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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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"3"
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]
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},
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"execution_count": 18,
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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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"lca(M,Node(2),Node(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": 19,
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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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"7"
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]
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},
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"execution_count": 19,
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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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"lca(M,Node(6),Node(8))"
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]
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},
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{
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"cell_type": "code",

‎Lesson 3 - Sorting.ipynb renamed to ‎Lesson 03 - Sorting.ipynb

File renamed without changes.

‎Lesson 4 - Dynamic Programming.ipynb renamed to ‎Lesson 04 - Dynamic Programming.ipynb

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@@ -251,6 +251,46 @@
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"coin_change(13, [1,3,7])"
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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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"source": [
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"from functools import lru_cache\n",
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"def coin_change_memo(coins,target):\n",
262+
"\t\n",
263+
"\t@lru_cache\n",
264+
"\tdef _coinchange(x):\n",
265+
"\t\tif x==0:\n",
266+
"\t\t\treturn 0\n",
267+
"\t\telse:\n",
268+
"\t\t\tcandidates = [1+_coinchange(x-c) for c in coins if x-c>=0]\n",
269+
"\t\t\treturn min(candidates if candidates else -1)\n",
270+
"\n",
271+
"\treturn _coinchange(target)"
272+
]
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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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"3"
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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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"coin_change_memo([1,3,7],13)"
292+
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},

‎Lesson 5 - Graphs.ipynb renamed to ‎Lesson 05 - Graphs.ipynb

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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
5-
"execution_count": 24,
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"execution_count": 3,
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"id": "1c8dea41-b247-40c9-9e44-7012ced2db06",
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"metadata": {},
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"outputs": [],
@@ -325,6 +325,66 @@
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"count_num_ccs([[0,1],[1,2],[3,4]], 5)"
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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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"id": "cc0fb250-c012-4b15-be21-298d53bfedf2",
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"metadata": {},
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"outputs": [],
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"source": [
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"def count_num_ccs_bfs(edges, N):\n",
336+
" '''\n",
337+
" same as above but using bfs instead of dfs\n",
338+
" '''\n",
339+
" \n",
340+
" graph = [[] for _ in range(N)]\n",
341+
" for a,b in edges:\n",
342+
" graph[a].append(b)\n",
343+
" graph[b].append(a)\n",
344+
" \n",
345+
" seen = [False for _ in range(N)]\n",
346+
" \n",
347+
" def bfs(node):\n",
348+
" Q = deque([node])\n",
349+
" while Q:\n",
350+
" node = Q.popleft()\n",
351+
" seen[node]=True\n",
352+
" for neighbor in graph[node]:\n",
353+
" if not seen[neighbor]:\n",
354+
" #seen[neighbor]=True\n",
355+
" Q.append(neighbor)\n",
356+
" \n",
357+
" counter=0\n",
358+
" for i in range(N):\n",
359+
" if not seen[i]:\n",
360+
" counter+=1\n",
361+
" #seen[i]=True\n",
362+
" bfs(i)\n",
363+
" \n",
364+
" return counter"
365+
]
366+
},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "299c6c1f-d402-41d6-8e5e-e44a055a3080",
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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": 8,
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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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"count_num_ccs_bfs([[0,1],[1,2],[3,4]], 5)"
386+
]
387+
},
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{
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"cell_type": "markdown",
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"id": "44399bd3-384e-4580-aeeb-055f98086cc6",

‎Lesson 6 - Heaps.ipynb renamed to ‎Lesson 06 - Heaps.ipynb

Lines changed: 66 additions & 1 deletion
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@@ -246,10 +246,75 @@
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"num_rooms( [[0,30],[5,10],[15,20]] )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "771dd889-30ae-4e77-8b97-7af7bf33f90c",
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"metadata": {},
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"source": [
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"# Problem: get the k closest points to origin\n",
255+
"\n",
256+
"This is another problem of the type \"get the k smallest\" or \"get the k largest\". In these cases, a heap is always prefered. Why? It's a waste to sort the entire array!\n",
257+
"\n",
258+
"Let's say, the array has length 1M, and we want the top 5 smallest elements. Using the heap (instead of sorting) would be log(1M)/log(5) ~ 9X faster!"
259+
]
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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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"id": "aed6d460-4b9e-4376-9aa9-d8bfbd7620be",
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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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"8.58405934844036"
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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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"import numpy as np\n",
280+
"np.log(1e6)/np.log(5)"
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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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"id": "b4c43aa3-4062-47c4-bb44-ec018b3607bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"def kClosest(self,points,k):\n",
291+
" '''\n",
292+
" min-heap solution, O(Nlogk)\n",
293+
" Note we use negative squared distance here because python heapq only supports max-heap.\n",
294+
" '''\n",
295+
"\n",
296+
" def calc_dist(p):\n",
297+
" # return the negative squared distance\n",
298+
" return -(p[0]**2+p[1]**2)\n",
299+
"\n",
300+
" heap = []\n",
301+
" for i in range(k):\n",
302+
" dist = calc_dist(points[i])\n",
303+
" heapq.heappush(heap,(dist,i)) # <O(log(k))\n",
304+
"\n",
305+
" for i in range(k,len(points)):\n",
306+
" dist = calc_dist(points[i])\n",
307+
" if dist>heap[0][0]:\n",
308+
" heapq.heappop(heap)\n",
309+
" heapq.heappush(heap,(dist,i)) # O(log(k))\n",
310+
"\n",
311+
" return [points[heap[i][1]] for i in range(k)]"
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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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"id": "5f91ef16-8a5d-4db2-9065-75a3c7e1d143",
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"id": "45e36328-536c-4017-a2b7-fb6b70cc7cb3",
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"metadata": {},
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"outputs": [],
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"source": []

‎Lesson 7 - Greedy algorithms.ipynb renamed to ‎Lesson 07 - Greedy algorithms.ipynb

File renamed without changes.

‎Lesson 8 - bit operation.ipynb renamed to ‎Lesson 08 - Bit Operation.ipynb

File renamed without changes.

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