Sensors send out signals​

Jenna updated her status on a social network regarding her upcoming vacation. what information should jenna not provide when updating her status on a social networking site? a. her favorite quote b. her likes in popular culture c. her routine d. her opinions on social issues res

Your task this week is to write a very simple spam classifier in python. it will classify messages as either spam (unwanted) or ham (wanted). the program will have a set of spam_words, words that are known to appear in spam messages. that set is included in the template file spam.pypreview the document. you will also define a spam threshold which reflects the allowed percentage of spam words in the message. you'll compute a 'spam indicator', which is the ratio of spam words to the total number of unique words in the message. if the spam indicator exceeds the spam threshold, the message is classified as spam. otherwise it is classified as ham. we'll assume that the spam threshold is a constant and has a value of 0.10. your program will prompt the user for a message and then will print the corresponding spam indicator with two decimal digits and the corresponding classification (spam or ham). the program will be case insensitive. the spam words are detected whether they are in lower case or upper case or mixed case. each word, spam or not, is counted once (even if it appears multiple times in the message.) the program will remove punctuation from the message before identifying the words and computing the spam indicator. for example '! ' must be identified as the spam word 'now'.

Some of your friends have gotten into the burgeoning field of time-series data mining, in which one looks for patterns in sequences of events that occur over time. purchases at stock exchanges"what's being bought" are one source of data with a natural ordering in time. given a long sequence s of such events, your friends want an efficient way to detect certain "patterns" in them"for example, they may want to know if the four events buy yahoo, buy ebay, buy yahoo, buy oracle occur in this sequence s, in order but not necessarily consecutively. they begin with a collection of possible events (e.g., the possible transactions) and a sequence s of n of these events. a given event may occur multiple times in s (e.g., yahoo stock may be bought many times in a single sequence s). we will say that a sequence s is a subsequence of s if there is a way to delete certain of the events from s so that the remaining events, in order, are equal to the sequence s . so, for example, the sequence of four events above is a subsequence of the sequence buy amazon, buy yahoo, buy ebay, buy yahoo, buy yahoo, buy oracle their goal is to be able to dream up short sequences and quickly detect whether they are subsequences of s. so this is the problem they pose to you: give an algorithm that takes two sequences of events"s of length m and s of length n, each possibly containing an event more than once"and decides in time o(m + n) whether s is a subsequence of s.