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For four months I avoided all crowded driveway happy hours and cul-de-sac cocktail parties. I didn't set foot inside my church even during the brief time it was open. I didn't set foot inside a grocery store as my youngest daughter Mary Clare, who was quarantined with me for most of the summer, did all the shopping. VIDEO | 02:44 Life in the NBA bubble Los Angeles Times Lakers reporter Tania Ganguli gives a look at what life is like inside the NBA bubble at Disney World in Orlando, Fla. I wore a mask everywhere. I followed all the rules, but a couple of weeks ago I didn't follow my instincts. The coronavirus came out swinging. The weekend before my symptoms appeared, for the first time in four months, I met friends for two dinners at two socially distanced patio tables. Nobody is required to wear masks at the tables, so I removed my mask when I sat, as did my dining partners, and we left them off during the entire time we were at the table. I didn't do anything that was prohibited, right?
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You'll need to apply for the loan separately through your state housing finance agency (HFA). To be eligible, home buyers must: Buy and use the property as their primary residence Receive an HFA Preferred mortgage through a state HFA home-buyer program Purchase an eligible HomePath property Buying a home through the HFA Preferred Incentive Program works like this: Get approved for an HFA Preferred mortgage through your state's HFA home-buyer program Decide which HomePath home you'd like to buy Have your realtor request the HFA HomePath Incentive with the initial offer Receive up to 3% of closing cost assistance Participating in this program severely limits your purchase options. You must buy an eligible home, all of which are foreclosed. Foreclosed homes often require a lot of work. Factor in the time and money it would take to update the home to your liking before making any decisions. Read more about Fannie Mae properties here. Housing Choice Voucher (HCV) Homeownership Program Families that currently receive assistance from the Housing Choice Voucher program might be eligible for the HCV Homeownership Program.
Fat and fiber slow down digestion and keep you feeling fuller longer — usually a good thing, but not when things are already moving so slowly. My diet normally consists of a lot of legumes, whole grains, whole nuts, and raw vegetables, which were all way too much for my delicate digestive system to handle. Processing these foods by cooking them and/or puréeing them made them much easier to take; for example, I couldn't handle whole chickpeas, but hummus was tolerable. And no matter how good those french fries smell, deep-fried foods will most likely leave you feeling very ill. Even non-fried foods prepared with a lot of oil can be troublesome, which I discovered after a slightly oily Thai takeout meal. Difficult-to-Digest Foods I Avoided Legumes (except puréed in hummus and other dips) Whole grains Whole nuts (but nut butters were fine) Raw, fibrous vegetables (carrots, kale, broccoli, etc. ) Fried foods or foods prepared with a lot of oil Easily digestible, lightly seasoned foods are your friends.
Now, we will implement the code to find similarities on documents based on a query. The first thing that we have to do is to transform the query as a vector on the matrix that we have. Then, we calculate the similarities between them. And finally, we retrieve all documents that have values above 0 in similarity. The code looks like this, def get_similar_articles(q, df): print("query:", q) print("Berikut artikel dengan nilai cosine similarity tertinggi: ") # Convert the query become a vector q = [q] q_vec = ansform(q). toarray(). reshape([0], ) sim = {} # Calculate the similarity for i in range(10): sim[i] = ([:, i], q_vec) / ([:, i]) * (q_vec) # Sort the values sim_sorted = sorted((), key=lambda x: x[1], reverse=True) # Print the articles and their similarity values for k, v in sim_sorted: if v! = 0. 0: print("Nilai Similaritas:", v) print(docs[k]) print() # Add The Query q1 = 'barcelona' # Call the function get_similar_articles(q1, df) Suppose that we want to find articles that talk about Barcelona.
