{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem: Find how many times a sentence can fit on a screen.\n", "\n", "See the [LeetCode](https://leetcode.com/problems/sentence-screen-fitting/) problem page.\n", "\n", "
\n",
    "Given a rows x cols screen and a sentence represented by a list of non-empty words, find how many times the given sentence can be fitted on the screen.\n",
    "\n",
    "Note:\n",
    "\n",
    "A word cannot be split into two lines.\n",
    "The order of words in the sentence must remain unchanged.\n",
    "Two consecutive words in a line must be separated by a single space.\n",
    "Total words in the sentence won't exceed 100.\n",
    "Length of each word is greater than 0 and won't exceed 10.\n",
    "1 ≤ rows, cols ≤ 20,000.\n",
    "Example 1:\n",
    "\n",
    "Input:\n",
    "rows = 2, cols = 8, sentence = [\"hello\", \"world\"]\n",
    "\n",
    "Output: \n",
    "1\n",
    "\n",
    "Explanation:\n",
    "hello---\n",
    "world---\n",
    "\n",
    "The character '-' signifies an empty space on the screen.\n",
    "Example 2:\n",
    "\n",
    "Input:\n",
    "rows = 3, cols = 6, sentence = [\"a\", \"bcd\", \"e\"]\n",
    "\n",
    "Output: \n",
    "2\n",
    "\n",
    "Explanation:\n",
    "a-bcd- \n",
    "e-a---\n",
    "bcd-e-\n",
    "\n",
    "The character '-' signifies an empty space on the screen.\n",
    "Example 3:\n",
    "\n",
    "Input:\n",
    "rows = 4, cols = 5, sentence = [\"I\", \"had\", \"apple\", \"pie\"]\n",
    "\n",
    "Output: \n",
    "1\n",
    "\n",
    "Explanation:\n",
    "I-had\n",
    "apple\n",
    "pie-I\n",
    "had--\n",
    "\n",
    "The character '-' signifies an empty space on the screen.\n",
    "
\n", "\n", "* [Constraints](#Constraints)\n", "* [Test Cases](#Test-Cases)\n", "* [Algorithm](#Algorithm)\n", "* [Code](#Code)\n", "* [Unit Test](#Unit-Test)\n", "* [Solution Notebook](#Solution-Notebook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints\n", "\n", "* Can we assume sentence is ASCII?\n", " * Yes\n", "* Can we assume the inputs are valid?\n", " * No\n", "* Is the output an integer?\n", " * Yes\n", "* Can we assume this fits memory?\n", " * Yes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Cases\n", "\n", "* None -> TypeError\n", "* rows < 0 or cols < 0 -> ValueError\n", "* cols = 0 -> 0\n", "* sentence = '' -> 0\n", "* rows = 2, cols = 8, sentence = [\"hello\", \"world\"] -> 1\n", "* rows = 3, cols = 6, sentence = [\"a\", \"bcd\", \"e\"] -> 2\n", "* rows = 4, cols = 5, sentence = [\"I\", \"had\", \"apple\", \"pie\"] -> 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm\n", "\n", "Refer to the [Solution Notebook](). If you are stuck and need a hint, the solution notebook's algorithm discussion might be a good place to start." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class Solution(object):\n", "\n", " def count_sentence_fit(self, sentence, rows, cols):\n", " # TODO: Implement me\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following unit test is expected to fail until you solve the challenge.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load test_count_sentence_fit.py\n", "import unittest\n", "\n", "\n", "class TestSolution(unittest.TestCase):\n", "\n", " def test_count_sentence_fit(self):\n", " solution = Solution()\n", " self.assertRaises(TypeError, solution.count_sentence_fit, \n", " None, None, None)\n", " self.assertRaises(ValueError, solution.count_sentence_fit, \n", " 'abc', rows=-1, cols=-1)\n", " sentence = [\"hello\", \"world\"]\n", " expected = 1\n", " self.assertEqual(solution.count_sentence_fit(sentence, rows=2, cols=8),\n", " expected)\n", " sentence = [\"a\", \"bcd\", \"e\"]\n", " expected = 2\n", " self.assertEqual(solution.count_sentence_fit(sentence, rows=3, cols=6),\n", " expected)\n", " sentence = [\"I\", \"had\", \"apple\", \"pie\"]\n", " expected = 1\n", " self.assertEqual(solution.count_sentence_fit(sentence, rows=4, cols=5),\n", " expected)\n", " print('Success: test_count_sentence_fit')\n", "\n", "\n", "def main():\n", " test = TestSolution()\n", " test.test_count_sentence_fit()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution Notebook\n", "\n", "Review the [Solution Notebook]() for a discussion on algorithms and code solutions." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 1 }