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1. Algorithm Design and Analysis

One important aspect of algorithm design is problem-solving strategies. This involves breaking down a complex problem into smaller, more manageable subproblems. By solving these subproblems, we can then combine their solutions to solve the original problem. This approach is known as the divide-and-conquer method.

Another important aspect of algorithm design is understanding the time and space complexity of an algorithm. Time complexity refers to the amount of time an algorithm takes to run, while space complexity refers to the amount of memory an algorithm requires. By analyzing the time and space complexity of an algorithm, we can determine its efficiency and scalability.

For example, let's consider the problem of finding the largest number in a list. One possible algorithm is to iterate through the list and keep track of the largest number encountered so far. This algorithm has a time complexity of O(n), where n is the size of the list. This means that the algorithm's

VikParuchuri

def code_filter(code):
    """
    Filters out low quality training code, with an aim to improving benchmark performance.  Works best with Python.
    """
    # Python keywords
    keywords = ["assert", "async", "await", "break", "class", "continue", "def", "del", "elif", "else", "except",
                "finally", "for", "from", "global", "if", "import", "in", "is", "lambda", "nonlocal", "not", "or",
                "pass", "raise", "return", "try", "while", "with", "yield"]
    # This covers single line comments in python + other common languages
    comment_symbols = ["#", "//"]

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import os
import re
import yaml
import textwrap
import html2text

def write_to_yaml(k, v, yaml_data):
    if isinstance(v, str):
        v = v.strip()
        if v.count("\n") == 0 and k not in ["left_text", "initial_display", "hint", "answer", "check_val", "imports", "instructions"]:

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*  Install brew packages (brew-packages.txt)
*  Setup all packages that need it so that they start at startup
*  Install pip (easy_install pip)
*  Install virtualenvwrapper (pip install virtualenvwrapper)
*  export WORKON_HOME=~/Envs
*  mkdir -p $WORKON_HOME
*  source /usr/local/bin/virtualenvwrapper.sh
*  mkvirtualenv turfly
*  export ARCHFLAGS="-Wno-error=unused-command-line-argument-hard-error-in-future";
psql -d postgres

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from queue.models import Submission
import queue
subs = Submission.objects.filter(queue_name="open-ended", retired=False)
queue_name="open-ended"
for sub in subs:
    qitem=str(sub.id)
    qcount = queue.producer.push_to_queue(queue_name, qitem)

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import networkx as nx
from nltk.tokenize.punkt import PunktSentenceTokenizer
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer

SENTENCES_IN_SUMMARY = 10
MIN_SENTENCE_LENGTH = 50
MAX_SENTENCE_LENGTH = 200

def f7(seq):
    seen = set()

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from copy import deepcopy
def invert(X):
    """
    Invert a matrix X according to gauss-jordan elimination
    In gauss-jordan elimination, we perform basic row operations to turn a matrix into
    row-echelon form.  If we concatenate an identity matrix to our input
    matrix during this process, we will turn the identity matrix into our inverse.
    X - input list of lists where each list is a matrix row
    output - inverse of X
    """

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class QuerySignatureV1AuthHandler(QuerySignatureHelper, AuthHandler):
    """
    Provides Query Signature V1 Authentication.
    """

    SignatureVersion = 1
    capability = ['sign-v1', 'mturk']

    def _calc_signature(self, params, *args):

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class QuerySignatureV1AuthHandler(QuerySignatureHelper, AuthHandler):
    """
Provides Query Signature V1 Authentication.
"""

    SignatureVersion = 1
    capability = ['sign-v1', 'mturk']

    def _calc_signature(self, params, *args):
        boto.log.debug('using _calc_signature_1')