Thursday, September 1, 2005
I got up by 5 AM so I could read my scriptures and be ready to go to work early. Bob prepared biscuits and gravy for breakfast this morning. Bob, Bruce and I started removing shingles by 7 AM. Bob was soon very tired so I had him quite and get down. When we got the one side cleaned off, I started laying new shingles while Bruce kept removing the old shingles from the other side. I took a short nap after we had lunch. About 5 PM we quite again and I took a short nap. We worked a little longer until Micheline had dinner ready. After eating I again laid shingles until dark. I went downstairs at 9:30 PM and wrote my journal entry before showering. When I got out of the shower I found that Mae had sent me her garments by mistake.
Friday, September 2, 2005
We didn’t get up until 5 AM again this morning. Both Bob and I slept in recliners last night as we didn’t sleep to good the night before. My stomach was bothering me when we went to bed last night. Bob tried to help us again today but he only lasted for a few minutes. I had him get down as I was worried about his health. I finished shingling the back side as Bruce worked on stripping the front. We had to strip the front right to the sheathing as that side was completely deteriorated. I again took a nap after lunch. We worked until almost 5 PM when we got down and I took another short nap. We worked a while longer and then ate dinner and worked until dark. It really helped me when Bruce was able to keep me supplied with shingles. After stopping for the day, Bruce, Micheline and I visited for a while and ate watermelon. Mae called and we visited for a few minutes. She had attended her aunt Merlynn’s funeral today. Mark had came up to get Bob and Mae sent me up some garments with him. Mark and Bob went up to Victor to attend a school reunion. I was very tired tonight.
Saturday, September 3, 2005
We got an early start on the roof but soon found that we were out of shingles. I called Mae and had her go to Roofer Supply in Ogden and get me another square of shingles and a package of Ridge and Hip shingles. I had thought that 2 packages of shingles would do it, but as I made the order I asked for a full square. I was sure glad I did because we only had 3 shingles left when we finished. From Bruce’s measurements I had figured it would take a total of 11 square but I was closer to 13 square. I am not sure whose fault it was on the figuring but I am sure glad Mae was able to bring the other up to us. Micheline had lunch ready when we finished the roof. I repaired some of the Sufis on the back eves before quitting. It was 3 PM when I headed for home. I had to stop in Logan and get natural gas for the pickup. Regular gas is too expensive when I can use natural gas. It was good to get home. We ordered Chinese food to be delivered and we relaxed and watched a couple of shows on TV.
Sunday, September 04, 2005
We slept in until 7 AM and so had to hurry a little to get to Church on time. I had filled out our donation slip and in the hurry had made an error. Bother Rogers called to see what I wanted to do with the $30 extra so we put it into Humanitarian Aid for the hurricane relief. We had enough Chinese food left over that we were able to have it this evening. Bob came home after he returned and told me of the experience he had with Mark’s driving. It was really quite funny as I had just told Mae that I was glad that I didn’t go with them as I was sure Bob would be upset with Mark and his driving. It has sure been nice to relax today. We started to bed fairly but then we rubbed each others feet and talked for a while. It was almost 11 PM when we went to sleep.
Monday, September 05, 2005
We got up at 6 AM and started getting ready to have our family come. I cooked us an egg omelet for breakfast. Both Mae and I worked all day getting ready for our family to come.  I had mowed part of the back yard before starting the fire. Bob came out and finished mowing the back lawn. About noon I put three Dutch Ovens on to cook. We had corn on the cob. Sandi came and got a peach cobbler ready to cook. By 2:30 PM most of our family had arrived and we started to eat. We had plenty for everyone. We had Bob, Carroll and Mark come and eat with us. Nick didn’t come with Sandi as he was working but he came later. Jeff and Gail came late as they were with Gail’s family this morning. With the exception of Shellie’s family we had all of our family here. After eating we visited and played games. Some of us played Horse Shoes, some played Frisbee and others played volleyball. I guess it was around 6 PM when all of them had left and then Mae and I had some cleanup to do. We were sure tired when we finished. We rubbed each others feet. I fell asleep in the chair for a while. We took hot bath to relieve some of the back pain and applied Thera-gesic.
We have decided to send Roger Sylvia the $300 dollars that he feels he needs. This is to be a loan, we will see. We received a letter from Loreta P. Emdrina from the Guimba 3rd Branch. We think she might be the President of the Primary. She has a sad story and would like financial aid. There are so many and how or who do you help?
Tuesday, September 06, 2005
Mae let me sleep in until almost 8 AM and then we spent some time together. Bob had cooked some extra sausage so sent some down for us. After breakfast I put the tools away that I had taken to do Bruce’s roof. Mae made a lunch and we drove up to the Uinta’s to look at some property. Before we left Roy we stopped and sent a Western Union money transfer to Roger Sylvia in the Philippines. We stopped in Evanston to get natural gas and found that they had moved the pump location to Quest Star yard. The lady that was at the office, let me fill up there. We checked at the Bear River Lodge but Adam Pulley wasn’t there. We went to the lots and hiked up through them. We got to far west and went on some other property. We will have to check on some things with the county and state before making any decision. We started reading the book “Natural Cures They Don’t Want You to Know About” by Kevin Trudeau. It gives you quite a bit to think about. Mae warmed up some dinner after we got back home. We watched a DVD “For Ever”. I had to make my journal entry before we went to bed.
Wednesday, September 07, 2005
After getting up and getting ready for the day, I prepared breakfast of French toast and eggs. I then spent the morning working on David’s garage plans. This afternoon I got materials priced out on the plans. BMC told me that the bid would only be good until Friday as they are having material prices jump up so often. I got the lock changed on my pickup cab back door but it doesn’t work much better that the old one. Mae and I went to the Ogden Temple this evening. After getting home we watched TV for a while.
Thursday, September 08, 2005
I cooked breakfast of pork chops and fried potatoes. I worked most of the day on David’s plans. Mae took Georgia out to dinner as it is Georgia’s Birthday today. David stopped by to look at the plans and the pricing. He said that they decided to wait for a few months before doing the job. Mae and I watched a show on the Hallmark channel this evening. It was late when we got to bed.
Friday, September 09, 2005
I spent all morning finishing up David’s plans. Mae was busy with the house. Mae left about 11 AM to run some errorns and then to take Kylan and Ashley to Dinosaur World. I worked on the front sprinkler system all afternoon and was unable to get it repaired. I finally told Bob to get a new valve and I would install it tomorrow. We went to a dance with Georgia and Rick. It was put on by the three Kaysville Stakes. They had a band called “Phat Daddy” and they were fair but too loud. Bishop Kent and Darla Rampton came mainly to visit with us. We visited with Rick and Georgia when we went back to their place so it was 11:58 PM when we got home.
Saturday, September 10, 2005
Irven woke us at 8 AM asking if they could bring their things snowy equipment over today. At first I said yes but then I called and ask them if I could have a week to get ready. I spent the rest of the day working on the sprinkler system. I replaced the one valve, changed the one sprinkler head to another line and replaced the electrical line to the one timer. I think it should work ok now. Jeff called and said that they were at a fund raiser for the hurricane victims and there was a motor scoter for sale for $200 and wanted to know if I would like to buy it. I told him to check it out and if it looked good get it. He did and now he is enjoying riding it. Mae has been busy all day cleaning house. She prepared for Smoky & Sylvia Barns to come for dinner at 5 PM but they didn’t show up. Mae called and left a message for them but when they didn’t reply she called Irven and Sandy who wasn’t home. She then called Scott & Mishelle but they had other commitments. She called Bob and Carroll but they had just eaten. Irven, Sandi and Mark came over to bring some storage so we had just invited them when Sylvia called. We had to excuse our self with Irven and Sandy and we did have Smoky and Sylvia come. It was a bad start but ended up having a good evening. After eating we played a game of marbles and a game of knock. They went home about 10:30 PM. We soon went to bed as I have a Stake Priesthood meeting in the morning at 7 AM. Good night.
Sunday, September 11, 2005
I got up at 5:30 AM and prepared to attend Stake General Priesthood meeting. Bob and Mark were also ready and we arrived there just before 7 AM. It was a very good meeting and I enjoyed the Spirit that was there. The meeting was well attended. It was quite chilly this morning and you can feel Fall coming. Mae had breakfast ready when I returned home. We then attended the regular Sunday Meetings. Bob and Carroll invited us for dinner after meetings so I ate little as we had invited Jeff, Gail and Marcie for dinner at 5 PM. I took a nap later. Shortly before 5 PM Jeff called and said that they were trying to get Gail into a Doctor as she is in quite a bit of pain. They weren’t able to come to dinner. We put things up and warmed up some stew. David invited us to come up this evening so we went up and played games and visited. We got home at 9:30 PM and called Jeff. They were just leaving the hospital. Gail has a tubular pregnancy and she will have to go into the Dr. tomorrow and have it checked and probably aborted.
Monday, September 12, 2005
I woke early and after using the bathroom and reading my scriptures I went back to bed for a while. It was 9 AM before we ate breakfast. I took bob with me and we went looking for tool boxes & shelving for my trailer. I bought a small bench press that was on sale and then it had another $5.00 off as the box had been destroyed. I got home about 4:30 PM and helped Mae get dinner ready. Melia (Terry & Angela’s daughter), Troy, Weston and Mckayla came over for dinner and Family Home Evening. We had a wonderful evening, with then leaving after 8:30 PM. We tried to reach Jeff and Gail to see what the results were. Jeff said the Doctor wants her to come back tomorrow. Jeff has been riding the motor scoter; he bought for me and said it needs a tune up. I told him to have it done. We started for bed at 10 PM but then we remembered that we hadn’t call David to wish him a happy Birthday so we did that. Then Bryce called to return my call from this morning. I asked him if I could come down tomorrow and borrow his welder to work on my trailer.
Tuesday, September 13, 2005
I slept in until 8 AM so that put me late getting to Bryce’s and going there always takes longer than planned. I worked on our bank statement until after 2 PM. Bob and I went over to Ogden and checked with a couple of places for shelving but didn’t find anything. Mae had dinner ready when I got home and then Bob and I went Home Teaching. We visited the Jackson’s but Darren wasn’t home. The Mitchell’s left a note on their front door telling us they would have to reschedule for next week. I tried to find something on TV but there wasn’t much so I shut it off. I started writing on some of my personal history and wrote for over 1 ½ hours. Boy there is a lot to remember and write down and the more I write the more I remember. I quite at 10:45 PM and we went to bed.
Wednesday, September 14, 2005
I got up at 6:30 but let Mae sleep until almost 8 AM as she hadn’t slept very well last night. Bob and I went to Salt Lake looking at shelving for my trailer. We were able to find some that I bought that was about 1/3 off any thing else we have looked at and much better quality. We stopped at Wholesale Electric and bought some parts and then went over to Home Depot and bought some more material. After getting home and eating I took a nap and then we started installing the electrical into the trailer. Mae has been sorting through the storage items and preparing for dinner this evening as she has invited Irven, Sandy, Brice, Deanna and Bob for dinner. We had a very enjoyable evening as we ate and visited. Irven and Sandy stayed and we played games. It is 11 PM and I have to be at Randy’s at 5 AM to go fishing with him in the morning. Good Night.
Thursday, September 16, 2005
This morning Randy and I left to go fishing at 5 AM. We were at Strawberry Reservoir about 7:30 and fishing by 8 AM. Fishing was steady all day with most of what we caught we had to release because of size. On Strawberry fish have to b e less than 16” or over 21” and most of what we caught was between that. I believe it was really good therapy for Randy to enjoy the activity and to talk. The day was beautiful and pleasant. The afternoon wind came up but never did get very hard. After loading the boat we cleaned the fish and then I went into the lodge and found out the prices for staying over night. We stopped in Heber City at Caroline’s Frozen Yogurt and bought a Yogurt to eat on the way home. It was after 9 PM when I got home after putting the boat away at Randy’s. I was very tired and soon took a shower and went to bed. Mae was busy all day with the house. She is still sorting and filling storage items and the many articles she has collected over the years.
Friday, September 16, 2005
Mae got up early and went up to David & Shauna’s to tend the children for them while Shauna takes a class in nursing that she has to take in order to keep her Nursing Certificate. I slept until 7:30 before getting up. After getting dressed and etc. I needed to go over to Ogden to sign some papers on the loan I had given JAY BIRD INVESTMENTS. Sherrie called and asked Bob if he could take her car into Lelis Transmission. I need to set up an appointment to have our car and pickup serviced and so told Bob I would meet him there and bring him home. When I went to US Title they told me that Mae needed to also sign the papers. Geralyn let me take the papers to Mae and then bring Mae’s drivers license back. I set the appointment at Lelis’s and when Bob got there we went over to Home Depot where I bought a bench vise and a bench grinder. I was looking for some electrical parts and would have had to buy a complete roll of flexible conduit but a man that was buying some electrical part said if I would buy the few fittings he needed he would give me the parts I needed. He is a electrician and had the parts I needed. That was fair for me because I only needed 18” of the flexible conduit and his parts came to less that the conduit I would have had to buy. We went up to David’s and Mae signed the paper. I got my generator while I was there. We took the papers back to US Title Co. and got our check. We made $556.53 on that loan so it was very worth while. We next went out to get natural gas in my pickup, came back and deposited the money in the bank in Roy. By the time we finished and got home it was 2 PM. I got some lunch and laid down for a ½ hour nap. Bob helped me and we made a stand for Irven. That took me longer than I had thought. Mae came home just as I was finishing. She went with me and we took the stand over to Irven & Sandy’s. Irven is doing worse today. We visited with them for a while. I wasn’t feeling the best when we came home. We had some fresh peaches and cottage cheese for super before I entered my journal entry and got ready for bed. Mae was tired from tending children today. She puts a lot into it but she wants to be a good Grandmother.
Saturday, September 17, 2005
We got up and got going around 8 AM. I flayed the fish and got them taken care of. Mae was on the phone for a while with Sister Smith. I worked on my trailer until almost 4 PM. We hurry to get to Maddox’s where we were to meet with other friends for dinner. We were early so went across the road to a fruit stand where we bought a ½ bushel of peaches for $8.00 and 12 ears of corn for $1.00 and a watermelon at 15 cents a lb. Dennis & Eleanor and Margie & Gary came but Ron was sick so he and Pat couldn’t make it. After eating we went over to Margie & Gary’s where we visited and we taught them how to play “Knock”. It was 10:30 before we got home. I carried in the items we bought while Mae made up some whey and put dishes away. I filled in my journal for today. We read the lesson on the way home and it made me discouraged as it is about raising your family in the Gospel and we are concerned as to how Sandi is doing and very concerned about David and Jeff.
Sunday, September 18, 2005
We got up at 6:45 AM but I just couldn’t get over the depression from last night. It was really a struggle for me to go to meetings this morning. I went fasting sa I just didn’t feel like eating. Meetings were good. We came home and got something for lunch. Ma3e and I then walked over to Von Nederhouser’s and wished them well on their mission call. I went on to attend Ward Welfare meeting and Mae came on home. When I got home I took a nap. Mae had invited Scott and Mishelle and the children over for dinner but Mishelle was helping her Aunt so she didn’t come. Mae cooked corn beef patties for dinner. We enjoyed visiting and playing games with Scott, Kylan and Ashley. They left about 8:30 PM. We did some more cleaning from dinner and the games. I called Brother & Sister Johnston and set up a time to go Home Teaching to them. I then filled in my journal entry.
Monday, September 19, 2005
We got up at 6:30 AM and I thought I had plenty of time to make the appointment to have the pickup into Lelis Transmission. I had prepared breakfast and we were saying morning prayers when I realized I was already late leaving. I ran to get my keys and ran out the door telling Mae that I had to go. She finished getting my breakfast ready so I could eat as I drove. I was 15 minutes late getting there but they were late getting his mechanics there so it worked out. When I got back home Bob came and helped me work on the trailer. Becky brought Tayler, Maddi and Jessica up and Mae took the girls to Dinosaur Land in Ogden. They didn’t get back until 4:30 PM. I was suppose to have been showered and ready to go when they got home. I really had to hurry. We got there at the same time Mike got home from work. Becky had prepared a nice roast dinner. We visited and enjoyed being there. We left a little after 9 PM. for home. We had to stop for gas before getting on the freeway. It was 10:30 PM when we got home and we soon went to bed.
Tuesday, September 20, 2005
I took the car in first thing this morning and got the transmission serviced. Bob helped me and while it was being serviced we picked up materials for the trailer. Mae went to the Dr. this morning to have her feet checked. She is having quite a bit of leg cramps. I worked on the trailer most of the day. This evening Bob and I went Home Teaching to the Mitchell’s and the Johnston’s. Mae put up peaches this evening and I helped her a little when I got home from H.T.
Wednesday, September 21, 2005
We were late getting up and so it just carried over with not getting started until 10 AM. Kip called to see if I wanted to have him give my name to those that had small job. I told him that would be ok. Steve ____ soon called to have me talk to them about finishing a bathroom for them. Mae went over to Scott & Mishelle’s to help Mishelle can peaches. Bob helped me work on my trailer all day. We were able to get the welding done before quitting at 6 PM. Mae asked me to plant a rose bush for her and to install the storm window in the storm door. We ate dinner and then turned on a show on the TV. I filled some capsules while watching the show. We watched a couple of M.A.S.H. shows before going to bed. I also filled in my journal during commercials.
Thursday, September 22, 2005
We enjoyed being together this morning but I had an appointment with Steve and Linda Stone in Layton to visit with them about finishing a ¼ bath and wash room for them. I was there until 11 AM. Mae went over to the Hook Fishing shop and got her fishing license and a fishing pole and reel. I was going to meet her there but didn’t make it. From there she went to the Davis School District office and meet Jackie. They had lunch together and went walking. When I got home I prepared some lunch and then worked the rest of the afternoon on my trailer. When I came in for super we started getting ready to go fishing tomorrow. Mae went to the store for a few groceries. I took a look at her pole and real and couldn’t get the drag on the real to work. I went upstairs and had bob look at it and checked to see if he had a real but he doesn’t. Irven and Sandy stopped in to visit for a few minutes. Mae gave me a hair cut this evening. I then showered and got ready for bed.
Friday, September 23, 2005
We got up early and met Judy and Randy at their home. We soon were packed up and left for Strawberry Bay Reservoir. We got there by 8 AM, checked in with the lodge and were soon fishing. Mae caught the first fish but as normal we had to release it because it was over 16” and less than 21”. It was very enjoyable having Mae and Judy with us. We fished all day and were able to keep 6 fish. We went to the restraint and got some take out for dinner.  We checked in at the lodge and got our rooms. We ate in Randy & Judy’s room and then played a game of “Knock”.
Saturday, September 24, 2005
We met Randy and Judy at the restraint at 7 AM for breakfast. We were able to start fishing at 8 AM. Again we were able catch several fish but were only able to keep 4 because of size. We quite fishing at 11:30 AM and loaded up the boat. We stopped at Caroline’s Frozen Yogurt as we went through Heber City. It was 2:30 PM when we got home. We took Mae home first so that she could get ready to attend the Relief Society Conference Session at the Conference Center with our Wards Sisters. After getting home I showered and took a nap. Bob then went with me to Willard where we bought four boxes of peaches. I flayed the fish we brought home. I watched a show on TV and filled in my journal. Mae called and told me that they were stopping for some dinner before coming home. It was late when we got to bed.
Sunday, September 25, 2005
We slept in and did not want to get up but know that we would have to hurry to get to church on time. Meetings went well although I wish I could be more alert by the time Sacrament started as I often fall asleep during the meeting. I again did that today. We came home and took a nap. We worked together preparing dinner and came up with a nice meal. I called Roy and visited for a while with him. Barbara has been in the hospital with meningitis and has just got out. Roy and Linda have had the children but Russell is going to come and get them tomorrow. They have received a call from the Missionary department asking for some more information from their Doctor before sending them a mission call. Bob and Carroll had some of their family come so we went up stairs and visited a few minutes with them. I recorded a documentary on channel 11 about the Prophet Joseph Smith. Jeff and Gail came over and we visited and played games. They left shortly after 9 PM so I downloaded some pictures from our camera and entered my journal. It has been a very enjoyable day.
Monday, September 26, 2005
I got up a little after 6 AM but let Mae sleep until 7 AM. I prepared breakfast of fish, fried potatoes and eggs. Mae helped Shauna bottle peaches and then she went to American Fork and helped Becky put up peaches. I worked on my trailer all day but it didn’t seem that I got a lot done. Bob didn’t help me today as he helped Carroll put up peaches. Mae didn’t get home until after 7 PM. She was tired but still kept going. I lied down and watch a documentary about the Prophet Joseph Smith before we went to bed.
Tuesday, September 27, 2005
Mae and I went into Ogden this morning and looked at a boat. It is an older boat but we are thinking of getting it. I made an appointment with Fred’s Marina to check the engine on the boat to see if it still is in fairly good condition. I will take it over tomorrow evening and they will check it out on Thursday morning. We also looked at a carport cover and are thinking we should get one. I talked to Bob and Carroll about putting a car port in. we will see how they feel about it. I worked down to Randy and Judy’s this afternoon, repairing a water line. I was able to remove the line, repair it and pull it back under their back addition. I did not get it completely finished before time to quite so I will have to return tomorrow. I took my trailer with me on this job and it went well. Mae prepared small red potatoes with peas in cream tuna gravy for diner. It was very good. This evening Mae and I went over to Robin Walker’s where I gave Robin and her son Kyson a Priesthood blessing. Robin’s husbands left her for another woman and have left the family in a real trial. Mae has been working all day on her lesson that is coming up.
Wednesday, September 28, 2005
I got up early this morning so I could go into Ogden and get the boat. Bob went in with me. Mae prepared breakfast after I got home. I then went over to Kaysville and took Mae’s fishing pole and reel back to get the reel fixed. The problem was that the line was slipping on the reel. They fixed that in just a few minutes and I was on my way. I stopped at Durk’s and bought a small tub cutter and then went down to Randy’s and finished repairing the water line. I also repaired a area in their soffit where the birds are getting in and nesting. Mae prepared a nice dinner of halibut, baked potatoes and backed squash. I took a nap after eating. Bob and I mowed the lawns and trimmed the edges. We took the boat over to Fred’s Marina to have the engine checked out. If the engine is ok then I will have them do some other work on the boat. I serviced the car this evening. Mark packed some of his items into my pickup so we can take them down to St. George tomorrow. I set up stairs and watched the TV and visited with Bob & Carroll. Mae had the Sisters jeer this evening for the book club. I took a shower and entered my journal before we went to bed. Mark could not get a hold of the man in Mesquite Nevada in order for us to go down there tomorrow to get Mark’s motivator. We will have to try another day. After the Sister’s left our place we got ready for bed and I filled in my journal.
Thursday, September 29, 2005
I started out this morning with plans to move more tools and materials into my trailer. Bob then invited us up for a waffle breakfast. That took until almost 10 AM. Irven called and asked if I could stop by and help them with a want ad for a cabinet they want to sale. Fred’s Marina called with their findings with the boat I asked them to check for me. There is quite a lot of work that the boat and motor needs and they felt it would be a unwise purchase. I stopped at Irven and Sandy’s then went over and got the boat. I returned it to its owner and told him that we wouldn’t buy it. On the way home I stopped and looked at tent trailers at two lots. The one that we looked at earlier had been sold and what I looked at today is much higher than I wanted to go. Bob checked with the city and they won’t let us put the driveway in like we want to. Bob and I looked through the paper and found a couple of trailers that we might be interested in. Mae and I watched a movie that Bob had recorded some time ago. We really enjoyed it. I filled in my journal entry and we went to bed about 11 PM.
Friday, September 30, 2005
Bob and I went to the Clinton city office this morning. I explained to those at the building office that we had a problem and would like to have their help in solving the problem. I explained that they had notified us that we could not park on the road, that we could not park on the grass and now we are told that we can not cut another driveway approach. They agreed that we could do none of those things but when I pushed for an answer they just became upset with us and said it was our fault that we had built the house where we did. We ended up leaving the office very upset but accomplishing nothing. We looked at some tent trailers but didn’t see anything that excited me. we found a fishing boat at Intermountain Boat & Truck Sales that I put $200 on it to hold it for me until they get it repaired so I can finish checking it out. It was 12:30 when we were going home. As we approached a Tamale store when Bob said he wanted to buy one so he could try them. I drove in and I asked to buy one for a sample to try. The clerk gave us each one to sample. We liked them so we bought some to take home. I ate 2 more for lunch. Mae took Tyler and Emily to Dinosaur Park. She got home after 3 PM. we changed clothes and went to Bountiful where we picked up the Clarks. We then went to the BYU Wilkinson Center in Provo where we enjoyed a Philippines Angeles Mission Reunion. It was a great evening being with and visiting with so many return missionaries from that mission. We enjoyed being with the Clarks and having them ride with us. It was after mid-night when we got home.


The display Property:
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Today is a sad commentary on nation’s democracy- Gov Dickson
by Rebecca AKINREMISERIAKE Henry Dickson, Bayelsa governor who is a member of the People’s Democratic Party, has described the ongoing gubernatorial election in the state a sad commentary on the nation’s democracy.
Dickson made the statement while speaking with reporters at the Polling Unit 5, Ward 2, Sagbama of Bayelsa where he cast his vote.
“Gentlemen of the press, today is a sad commentary on our nation’s democracy,” he told journalists on Saturday.
Accompanied with his wife and aides, Dickson said election had not taken place in Southern Ijaw, except Amassoma.
“As we are speaking, in this day and age, gun-wielding militiamen, with the aid of security forces, have overrun hapless electoral officials. Most of the electoral officials in Southern Ijaw, as we speak, are kidnapped and held hostage.
“Everyone knows APC has no structure in Bayelsa to win an election, but they have armed militias, surveillance contractors, who kill and maim for APC,” he said.
While Dickson described himself as a “man of peace” who would stand for peace and respect for human dignity, he accused the All Progressives Congress of recruiting the Nigerian army and officers of the Nigeria Police Force to work against citizens’ right to vote.
“This has been the lots, this has been what we have been managing since 2015, with the APC at the centre is not caring about the dignity of our people,” he said
He said the APC and federal government were “too hungry” to make Nigeria a one-party state.
The governorship election is ongoing in Bayelsa and Kogi states. While vote counting has been completed in some polling units in both states, voting is still ongoing at some polling units.
Meanwhile, there have been reported cases of violence, ballot snatching and vote-buying in both Kogi and Bayelsa States.
Just before 12 noon on Saturday, some thugs believed to be working for the PDP sold ballot box and disrupted the voting process at polling units 1 and 2, ward 2, Ekeki Yenogoa local government area of Bayelsa state.
Similar allegation has been reported against the opposition party, APC in the state.


We have designed the Relief algorithms to be integrated directly into scikit-learn machine learning workflows. Below, we provide code samples showing how the various Relief algorithms can be used as feature selection methods in scikit-learn pipelines.
For details on the algorithmic differences between the various Relief algorithms, please refer to this research paper.
ReliefF
ReliefF is the most basic of the Relief-based feature selection algorithms, and it requires you to specify the number of nearest neighbors to consider in the scoring algorithm. The parameters for the ReliefF algorithm are as follows:
| Parameter | Valid values | Effect |
|---|---|---|
| n_features_to_select | Any positive integer or float | The number of best features to retain after the feature selection process. The "best" features are the highest-scored features according to the ReliefF scoring process. |
| n_neighbors | Any positive integer | The number of neighbors to consider when assigning feature importance scores. If a float number is provided, that percentage of training samples is used as the number of neighbors. More neighbors results in more accurate scores, but takes longer. |
| discrete_limit | Any positive integer | Value used to determine if a feature is discrete or continuous. If the number of unique levels in a feature is > discrete_threshold, then it is considered continuous, or discrete otherwise. |
| n_jobs | Any positive integer or -1 | The number cores to dedicate to running the algorithm in parallel with joblib. Set to -1 to use all available cores. |
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import ReliefF
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
sep='\t', compression='gzip')
features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values
clf = make_pipeline(ReliefF(n_features_to_select=2, n_neighbors=100),
RandomForestClassifier(n_estimators=100))
print(np.mean(cross_val_score(clf, features, labels)))
>>> 0.795
SURF
SURF, SURF*, and MultiSURF are all extensions to the ReliefF algorithm that automatically determine the ideal number of neighbors to consider when scoring the features.
| Parameter | Valid values | Effect |
|---|---|---|
| n_features_to_select | Any positive integer | The number of best features to retain after the feature selection process. The "best" features are the highest-scored features according to the SURF scoring process. |
| discrete_limit | Any positive integer | Value used to determine if a feature is discrete or continuous. If the number of unique levels in a feature is > discrete_threshold, then it is considered continuous, or discrete otherwise. |
| n_jobs | Any positive integer or -1 | The number cores to dedicate to running the algorithm in parallel with joblib. Set to -1 to use all available cores. |
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import SURF
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
sep='\t', compression='gzip')
features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values
clf = make_pipeline(SURF(n_features_to_select=2),
RandomForestClassifier(n_estimators=100))
print(np.mean(cross_val_score(clf, features, labels)))
>>> 0.795
SURF*
| Parameter | Valid values | Effect |
|---|---|---|
| n_features_to_select | Any positive integer | The number of best features to retain after the feature selection process. The "best" features are the highest-scored features according to the SURF* scoring process. |
| discrete_limit | Any positive integer | Value used to determine if a feature is discrete or continuous. If the number of unique levels in a feature is > discrete_threshold, then it is considered continuous, or discrete otherwise. |
| n_jobs | Any positive integer or -1 | The number cores to dedicate to running the algorithm in parallel with joblib. Set to -1 to use all available cores. |
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import SURFstar
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
sep='\t', compression='gzip')
features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values
clf = make_pipeline(SURFstar(n_features_to_select=2),
RandomForestClassifier(n_estimators=100))
print(np.mean(cross_val_score(clf, features, labels)))
>>> 0.795
MultiSURF
| Parameter | Valid values | Effect |
|---|---|---|
| n_features_to_select | Any positive integer | The number of best features to retain after the feature selection process. The "best" features are the highest-scored features according to the MultiSURF scoring process. |
| discrete_limit | Any positive integer | Value used to determine if a feature is discrete or continuous. If the number of unique levels in a feature is > discrete_threshold, then it is considered continuous, or discrete otherwise. |
| n_jobs | Any positive integer or -1 | The number cores to dedicate to running the algorithm in parallel with joblib. Set to -1 to use all available cores. |
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import MultiSURF
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
sep='\t', compression='gzip')
features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values
clf = make_pipeline(MultiSURF(n_features_to_select=2),
RandomForestClassifier(n_estimators=100))
print(np.mean(cross_val_score(clf, features, labels)))
>>> 0.795
MultiSURF*
| Parameter | Valid values | Effect |
|---|---|---|
| n_features_to_select | Any positive integer | The number of best features to retain after the feature selection process. The "best" features are the highest-scored features according to the MultiSURF* scoring process. |
| discrete_limit | Any positive integer | Value used to determine if a feature is discrete or continuous. If the number of unique levels in a feature is > discrete_threshold, then it is considered continuous, or discrete otherwise. |
| n_jobs | Any positive integer or -1 | The number cores to dedicate to running the algorithm in parallel with joblib. Set to -1 to use all available cores. |
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import MultiSURFstar
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
sep='\t', compression='gzip')
features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values
clf = make_pipeline(MultiSURFstar(n_features_to_select=2),
RandomForestClassifier(n_estimators=100))
print(np.mean(cross_val_score(clf, features, labels)))
>>> 0.795
TURF
TURF advances the feature selection process from a single round to a multi-round process, and can be used in conjunction with any of the Relief-based algorithms. TURF begins with all of the features in the first round, scores them using one of the Relief-based algorithms, then eliminates a portion of them that have the worst scores. With this reduced feature set, TURF again scores the remaining features and eliminates a portion of the worst-scoring features. This process is repeated until a predefined number of features remain.
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import TuRF
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
sep='\t', compression='gzip')
features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values
headers = list(genetic_data.drop("class", axis=1))
clf = make_pipeline(TuRF(core_algorithm="MultiSURF", n_features_to_select=2, step=0.1),
RandomForestClassifier(n_estimators=100))
print(np.mean(cross_val_score(clf, features, labels, fit_params={'turf__headers': headers})))
>>> 0.795
Acquiring feature importance scores
In some cases, it may be useful to compute feature importance scores without actually performing feature selection. We have made it possible to access all Relief-based algorithm's scores via the feature_importances_
attribute. Below is a code example showing how to access the scores from the ReliefF algorithm.
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import ReliefF
from sklearn.model_selection import train_test_split
genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
sep='\t', compression='gzip')
features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values
# Make sure to compute the feature importance scores from only your training set
X_train, X_test, y_train, y_test = train_test_split(features, labels)
fs = ReliefF()
fs.fit(X_train, y_train)
for feature_name, feature_score in zip(genetic_data.drop('class', axis=1).columns,
fs.feature_importances_):
print(feature_name, '\t', feature_score)
>>>N0 -0.0000166666666667
>>>N1 -0.006175
>>>N2 -0.0079
>>>N3 -0.006275
>>>N4 -0.00684166666667
>>>N5 -0.0104416666667
>>>N6 -0.010275
>>>N7 -0.00785
>>>N8 -0.00824166666667
>>>N9 -0.00515
>>>N10 -0.000216666666667
>>>N11 -0.0039
>>>N12 -0.00291666666667
>>>N13 -0.00345833333333
>>>N14 -0.00324166666667
>>>N15 -0.00886666666667
>>>N16 -0.00611666666667
>>>N17 -0.007325
>>>P1 0.108966666667
>>>P2 0.111
Higher positive scores indicate that the features are likely predictive of the outcome, whereas negative scores indicate that the features are likely noise. We generally recommend removing all features with negative scores at a minimum.


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Categories painstakingly copy-pasted from Job classification
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4 ANOVA
4.2 Purpose and aim
Analysis of variance or ANOVA is a test than can be used when we have multiple samples of continuous data. Whilst it is possible to use ANOVA with only two samples, it is generally used when we have three or more groups. It is used to find out if the samples came from parent distributions with the same mean. It can be thought of as a generalisation of the two-sample Student’s t-test.
4.4 Data and hypotheses
For example, suppose we measure the feeding rate of oyster catchers (shellfish per hour) at three sites characterised by their degree of shelter from the wind, imaginatively called exposed
(E), partially sheltered
(P) and sheltered
(S). We want to test whether the data support the hypothesis that feeding rates don’t differ between locations. We form the following null and alternative hypotheses:
- \(H_0\): The mean feeding rates at all three sites is the same \(\mu E = \mu P = \mu S\)
- \(H_1\): The mean feeding rates are not all equal.
We will use a one-way ANOVA test to check this.
- We use a one-way ANOVA test because we only have one predictor variable (the categorical variable location).
- We’re using ANOVA because we have more than two groups and we don’t know any better yet with respect to the exact assumptions.
The data are stored in the file CS2-oystercatcher.csv
.
4.5 Summarise and visualise
First we read in the data.
oystercatcher <- read.csv("data/raw/CS2-oystercatcher.csv")
Next we summarise the data and visualise them. We have a quick peek at the first few rows of our data with head()
so we can see how the data are organised.
The data are in stacked format. The first column contains information on feeding rates and is called feeding.
The second column has categorical data on the type of site and is called site
.
head(oystercatcher)
## feeding site
## 1 14.2 Exposed
## 2 16.5 Exposed
## 3 9.3 Exposed
## 4 15.1 Exposed
## 5 13.4 Exposed
## 6 18.4 Partial
aggregate(feeding ~ site, data = oystercatcher, summary)
## site feeding.Min. feeding.1st Qu. feeding.Median feeding.Mean
## 1 Exposed 9.30 13.40 14.20 13.70
## 2 Partial 13.00 16.50 17.40 17.14
## 3 Sheltered 21.50 22.20 24.10 23.64
## feeding.3rd Qu. feeding.Max.
## 1 15.10 16.50
## 2 18.40 20.40
## 3 25.10 25.30
boxplot(feeding ~ site, data = oystercatcher)
Looking at the data, there appears to be a noticeable difference in feeding rates between the three sites. We would probably expect a reasonably significant statistical result here.
4.6 Implement test
Perform an ANOVA test on the data:
The first line fits a linear model to the data (i.e. finds the means of the three groups and calculates a load of intermediary data that we need for the statistical analysis) and stores this information in an R object (which I’ve called lm_oystercatchers
, but which you can call what you like). The second line actually carries out the ANOVA analysis.
- The first argument must be in the formula format:
response ~ predictor
- If the data are stored in stacked format, then the second argument must be the name of the data frame
- The
anova()
command takes a linear model object as its main argument
4.7 Interpret output and report results
This is the output that you should now see in the console window:
## Analysis of Variance Table
##
## Response: feeding
## Df Sum Sq Mean Sq F value Pr(>F)
## site 2 254.812 127.406 21.508 0.0001077 ***
## Residuals 12 71.084 5.924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- The 1st line just tells you the that this is an ANOVA test
- The 2nd line tells you what the response variable is (in this case feeding)
- The 3rd, 4th and 5th lines are an ANOVA table which contain some useful values:
- The
Df
column contains the degrees of freedom values on each row, 2 and 12 (which we’ll need for the reporting) - The
F
value column contains the F statistic, 21.508 (which again we’ll need for reporting). - The p-value is 0.0001077 and is the number directly under the
Pr(>F)
on the 4th line. - The other values in the table (in the
Sum Sq
andMean Sq
) columns are used to calculate the F statistic itself and we don’t need to know these.
- The
- The 6th line has some symbolic codes to represent how big (small) the p-value is; so, a p-value smaller than 0.001 would have a *** symbol next to it (which ours does). Whereas if the p-value was between 0.01 and 0.05 then there would simply be a * character next to it, etc. Thankfully we can all cope with actual numbers and don’t need a short-hand code to determine the reporting of our experiments (please tell me that’s true…!)
Again, the p-value is what we’re most interested in here and shows us the probability of getting samples such as ours if the null hypothesis were actually true.
Since the p-value is very small (much smaller than the standard significance level of 0.05) we can say “that it is very unlikely that these three samples came from the same parent distribution” and as such we can reject our null hypothesis and state that:
A one-way ANOVA showed that the mean feeding rate of oystercatchers differed significantly between locations (F = 21.51, df = 2, 12, p = 0.00011).
Note that we have included (in brackets) information on the test statistic (F = 21.51), both degrees of freedom (df = 2, 12), and the p-value (p = 0.00011).
4.8 Assumptions
To use an ANOVA test, we have to make three assumptions:
- The parent distributions from which the samples are taken are normally distributed
- Each data point in the samples is independent of the others
- The parent distributions should have the same variance
In a similar way to the two-sample tests we will consider the normality and equality of variance assumptions both using tests and by graphical inspection (and ignore the independence assumption).
1. Normality
Unstack the data and perform a Shapiro-Wilk test on each group separately.
uns_oyster <- unstack(oystercatcher)
shapiro.test(uns_oyster$Exposed)
shapiro.test(uns_oyster$Partial)
shapiro.test(uns_oyster$Sheltered)
This is the output that you should now see in the console window:
##
## Shapiro-Wilk normality test
##
## data: uns_oyster$Exposed
## W = 0.9151, p-value = 0.4988
##
## Shapiro-Wilk normality test
##
## data: uns_oyster$Partial
## W = 0.96913, p-value = 0.8697
##
## Shapiro-Wilk normality test
##
## data: uns_oyster$Sheltered
## W = 0.88532, p-value = 0.3341
We can see that all three groups appear to be normally distributed which is good.
For ANOVA however, considering each group in turn is often considered quite excessive and, in most cases, it is sufficient to consider the normality of the combined set of residuals from the data. We’ll explain residuals properly in the next session but effectively they are the difference between each data point and its group mean. The residuals can be obtained directly from the linear model we fitted earlier.
Extract the residuals from the data and check their normality:
resid_oyster <- residuals(lm_oystercatcher)
shapiro.test(resid_oyster)
##
## Shapiro-Wilk normality test
##
## data: resid_oyster
## W = 0.93592, p-value = 0.3338
Again, we can see that the combined residuals from all three groups appear to be normally distributed (which is as we would have expected given that they were all normally distributed individually!)
2. Equality of Variance
We now test for equality of variance using Bartlett’s test (since we’ve just found that all of the individual groups are normally distributed).
Perform Bartlett’s test on the data:
bartlett.test(feeding ~ site, data = oystercatcher)
##
## Bartlett test of homogeneity of variances
##
## data: feeding by site
## Bartlett's K-squared = 0.90632, df = 2, p-value = 0.6356
Where the relevant p-value is given on the 3rd line. Here we see that each group do appear to have the same variance.
3. Graphical Interpretation and Diagnostic Plots
R provides a convenient set of graphs that allow us to assess these assumptions graphically. If we simply ask R to plot the lm
object we have created, then we can see some of these diagnostic plots.
Create the standard set diagnostic plots:
## hat values (leverages) are all = 0.2
## and there are no factor predictors; no plot no. 5
The second line creates the three diagnostic plots (it actually tries to create four plots but can’t do that for this dataset so you’ll also see some warning text output to the screen (something about hat values). I’ll go through this in the next session where it’s easier to explain).
- In this example, two of the plots (top-left and bottom-left) show effectively the same thing: what the distribution of data between each group look like. These allow an informal check on the equality of variance assumption.
- For the top-left graph we want all data to be symmetric about the 0 horizontal line and for the spread to be the same (please ignore the red line; it is an unhelpful addition to these graphs).
- For the bottom-left graph, we will look at the red line as we want it to be approximately horizontal.
- The top-right graph is a familiar Q-Q plot that we used previously to assess normality, and this looks at the combined residuals from all of the groups (in much the same way as we looked at the Shapiro-Wilk test on the combined residuals).
We can see that these graphs are very much in line with what we’ve just looked at using the test, which is reassuring. The groups all appear to have the same spread of data, and whilst the QQ-plot isn’t perfect, it appears that the assumption of normality is allright.
At this stage, I should point out that I nearly always stick with the graphical method for assessing the assumptions of a test. Assumptions are rarely either completely met or not met and there is always some degree of personal assessment.
Whilst the formal statistical tests (like Shapiro) are technically fine, they can often create a false sense of things being absolutely right or wrong in spite of the fact that they themselves are still probabilistic statistical tests. In these exercises we are using both approaches whilst you gain confidence and experience in interpreting the graphical output and whilst it is absolutely fine to use both in the future I would strongly recommend that you don’t rely solely on the statistical tests in isolation.
4.9 Post-hoc testing
One drawback with using an ANOVA test is that it only tests to see if all of the means are the same, and if we get a significant result using ANOVA then all we can say is that not all of the means are the same, rather than anything about how the pairs of groups differ. For example, consider the following boxplot for three samples.
Each group is a random sample of 20 points from a normal distribution with variance 1. Groups 1 and 2 come from a parent population with mean 0 whereas group 3 come from a parent population with mean 2. The data clearly satisfy the assumptions of an ANOVA test.
1. Read in the data and plot
2. Test for a significant difference in group means
## Analysis of Variance Table
##
## Response: response
## Df Sum Sq Mean Sq F value Pr(>F)
## group 2 33.850 16.9250 20.16 2.392e-07 ***
## Residuals 57 47.854 0.8395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Here we have a p-value of 2.39x10-7 and so the test has very conclusively rejected the hypothesis that all means are equal.
However, this was not due to all of the sample means being different, but rather just because one of the groups is very different from the others. In order to drill down and investigate this further we use a new test called Tukey’s range test (or Tukey’s honest significant difference test – this always makes me think of some terrible cowboy/western dialogue). This will compare all of the groups in a pairwise fashion and reports on whether a significant difference exists.
3. Performing Tukey’s test on these data
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = response ~ group, data = tukey)
##
## $group
## diff lwr upr p adj
## sample2-sample1 0.3037563 -0.3934982 1.001011 0.5498005
## sample3-sample1 1.7233591 1.0261047 2.420614 0.0000005
## sample3-sample2 1.4196028 0.7223484 2.116857 0.0000246
The first argument repeats our ANOVA using a different function ‘aov().’ We store the output of this function in an R object called aov_tukey
.
Note that the TukeyHSD()
function takes the output of the aov()
function as its argument and not the raw data.
The bottom three lines contain the information that we want. The final column of each (entitled p adj
) is the p-value that we’re looking for. The null hypothesis in each case is that there is no difference in the mean between the two groups. As we can see the first line shows that there isn’t a significant difference between sample1
and sample2
but the 2nd and 3rd lines show that there is a significant difference between sample1
and sample3
, as well as sample2
and sample3
. This matches with what we expected based on the boxplot.
4. Assumptions
When to use Tukey’s range test is a matter of debate (strangely enough a lot of statistical analysis techniques are currently matters of opinion rather than mathematical fact – it does explain a little why this whole field appears so bloody confusing!)
- Some people claim that we should only perform Tukey’s range test (or any other post-hoc tests) if the preceding ANOVA test showed that there was a significant difference between the groups and that if the ANOVA test had not shown any significant differences between groups then we would have to stop there.
- Other people say that this is rubbish and we can do what the hell we like, when we like as long as we tell people what we did.
The background to this is rather involved but one of the reasons for this debate is to prevent so-called data-dredging or p-hacking. This is where scientists/analysts are so fixated on getting a “significant” result that they perform a huge variety of statistical techniques until they find one that shows that their data is significant (this was a particular problem in psychological studies for while – not to point fingers though, they are working hard to sort their stuff out. Kudos!).
Whether you should use post-hoc testing or not will depend on your experimental design and the questions that you’re attempting to answer.
Tukey’s range test, when we decide to use it, requires the same three assumptions as an ANOVA test:
- Normality of distributions
- Equality of variance between groups
- Independence of observations
4.10 Exercise
Exercise 4.1 Juvenile lobster weight
Juvenile lobsters in aquaculture were grown on three different diets (fresh mussels, semi-dry pellets and dry flakes). After nine weeks, their wet weight was:
| Mussels | Pellets | Flakes |
|---|---|---|
| 151.6 | 117.7 | 101.8 |
| 132.1 | 110.8 | 102.9 |
| 104.2 | 128.6 | 90.4 |
| 153.5 | 110.1 | 132.8 |
| 132.0 | 175.2 | 129.3 |
| 119.0 | NA | 129.4 |
| 161.9 | NA | NA |
Is there any evidence that diet affects the growth rate of lobsters?
- Write down the null and alternative hypotheses
- Import the data into R
- The data are stored in the file
data/raw/CS2-lobsters.csv
- The data are stored in the file
- Summarise and visualise the data
- Check the assumptions using appropriate tests and graphical analyses
- Perform an ANOVA test
- Write down a sentence that summarise the results that you have found
- Perform a post-hoc test and report the findings
Answer
1. Hypotheses
\(H_0\) : all means are equal
\(H_1\) : not all means are equal
2-3. Import Data, summarise and visualise
The data are stored in a .csv
file in stacked format with columns called weight
and diet
.
lobsters <- read.csv("data/raw/CS2-lobsters.csv")
Let’s look at the data and see what we can see.
## weight diet
## 1 151.6 Mussels
## 2 132.1 Mussels
## 3 104.2 Mussels
## 4 153.5 Mussels
## 5 132.0 Mussels
## 6 119.0 Mussels
## 7 161.9 Mussels
## 8 117.7 Pellets
## 9 110.8 Pellets
## 10 128.6 Pellets
## 11 110.1 Pellets
## 12 175.2 Pellets
## 13 101.8 Flakes
## 14 102.9 Flakes
## 15 90.4 Flakes
## 16 132.8 Flakes
## 17 129.3 Flakes
## 18 129.4 Flakes
aggregate(weight ~ diet, data = lobsters, summary)
## diet weight.Min. weight.1st Qu. weight.Median weight.Mean weight.3rd Qu.
## 1 Flakes 90.4000 102.0750 116.1000 114.4333 129.3750
## 2 Mussels 104.2000 125.5000 132.1000 136.3286 152.5500
## 3 Pellets 110.1000 110.8000 117.7000 128.4800 128.6000
## weight.Max.
## 1 132.8000
## 2 161.9000
## 3 175.2000
boxplot(weight ~ diet, data = lobsters)
As always we use the plot and summary to assess three things:
- Did we load the data in properly?
- We see three groups with reasonable values. There aren’t any data points that are obviously wrong (negative, zero or massively big) and we have the right number of groups. So it looks as if we didn’t do anything obviously wrong.
- What do we expect as a result of a statistical test?
- Whilst the
Mussels
group does look higher than the other two groups,Pellets
andFlakes
appear almost identical in terms of average values, and there’s quite a bit of overlap with theMussels
group. A non-significant result is the most likely answer, and I would be surprised to see a significant p-value - especially given the small sample size that we have here.
- What do we think about assumptions?
- The groups appear mainly symmetric (although
Pellets
is a bit weird) and so we’re not immediately massively worried about lack of normality. Again,Flakes
andMussels
appear to have very similar variances but it’s a bit hard to decide what’s going on withPellets.
It’s hard to say what’s going on with the assumptions and so I’ll wait and see what the other tests say.
4. Explore Assumptions
Normality
We’ll be really thorough here and consider the normality of each group separately and jointly using the Shapiro-Wilk test, as well as looking at the Q-Q plot. In reality, and after these examples , we’ll only use the Q-Q plot.
We’ll need to unstack the data to use the Shapiro-Wilk test on the individual groups:
lobst_uns <- unstack(lobsters, weight ~ diet)
shapiro.test(lobst_uns$Flakes)
##
## Shapiro-Wilk normality test
##
## data: lobst_uns$Flakes
## W = 0.84368, p-value = 0.1398
shapiro.test(lobst_uns$Mussels)
##
## Shapiro-Wilk normality test
##
## data: lobst_uns$Mussels
## W = 0.94784, p-value = 0.71
shapiro.test(lobst_uns$Pellets)
##
## Shapiro-Wilk normality test
##
## data: lobst_uns$Pellets
## W = 0.76706, p-value = 0.0425
Flakes
and Mussels
are fine, but, as we suspected from earlier, Pellets
appears to have a marginally significant Normality test result.
Let’s look at the Shapiro-Wilk test for all of the data together:
resid_lobst <- residuals(lm(weight ~ diet, data = lobsters))
shapiro.test(resid_lobst)
##
## Shapiro-Wilk normality test
##
## data: resid_lobst
## W = 0.94779, p-value = 0.3914
This on the other hand says that everything is fine. Let’s look at the Q-Q-plot:
Here, I’ve used an extra argument to the normal diagnostic plots call. The default option is to plot 4 diagnostic plots, but you can tell R to only plot a specific one. (If you want to know more about this have a look at the plot.lm
help documentation using ?plot.lm
). I’ve asked R to only plot the Q-Q plot with the which = 2
argument.
The Q-Q plot looks OK, not perfect, but more than good enough for us to have confidence in the normality of the data.
Overall, I’d be happy that the assumption of normality has been adequately well met here. The suggested lack of normality in the Pellets
was only just significant and we have to take into account that there are only 5 data points in that group. If there had been a lot more points in that group, or if the Q-Q plot was considerably worse then I wouldn’t be confident.
Equality of Variance
We’ll consider the Bartlett test and we’ll look at some diagnostic plots too.
bartlett.test(weight ~ diet, data = lobsters)
##
## Bartlett test of homogeneity of variances
##
## data: weight by diet
## Bartlett's K-squared = 0.71273, df = 2, p-value = 0.7002
In the above code, I’ve used the same trick as before with the which
argument to only plot the two diagnostic plots that relate to equality of variance (residuals vs fitted and scale-location).
Here all three methods agree that there isn’t any issues with equality of variance:
- the Bartlett test p-value is large and non-significant
- the spread of points in all three groups in the residuals vs fitted graph are roughly the same
- the red line in the scale-location graph is pretty horizontal
Overall, this assumption is pretty well met.
5. Carry out one-way ANOVA
With our assumptions of normality and equality of variance met we can be confident that a one-way ANOVA is an appropriate test.
## Analysis of Variance Table
##
## Response: weight
## Df Sum Sq Mean Sq F value Pr(>F)
## diet 2 1567.2 783.61 1.6432 0.2263
## Residuals 15 7153.1 476.87
6. Result
A one-way ANOVA test indicated that the mean weight of juvenile lobsters did not differ significantly between diets (F = 1.64, df = 2,15, p = 0.23).
7. Post-hoc testing with Tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = weight ~ diet, data = lobsters)
##
## $diet
## diff lwr upr p adj
## Mussels-Flakes 21.895238 -9.661957 53.45243 0.2024851
## Pellets-Flakes 14.046667 -20.300196 48.39353 0.5508657
## Pellets-Mussels -7.848571 -41.061560 25.36442 0.8148766
Here we can see that actually, there is no significant difference between any of the pairs of groups in this dataset.
I want to reiterate that carrying out the post-hoc test after getting a non-significant result with ANOVA is something that you have to think very carefully about and it all depends on what your research question it.
If your research question was:
Does diet affect lobster weight? or Is there any effect of diet on lobster weight?
Then when we got the non-significant result from the ANOVA test we should have just stopped there as we have our answer. Going digging for “significant” results by running more tests is a main factor that contributes towards lack of reproducibility in research.
If on the other hand your research question was:
Are any specific diets better or worse for lobster weight than others?
Then we should probably have just skipped the one-way ANOVA test entirely and just jumped straight in with the Tukey’s range test, but the important point here is that the result of the one-way ANOVA test doesn’t preclude carrying out the Tukey test.


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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Scallops with wasabi is a striking combination, perfectly balanced here, served with some delicate sea vegetables.
For the wasabi sauce, heat a frying pan until warm, add the olive oil, shallots and a pinch of salt, cover with a lid and gently fry on a low heat for 10 minutes. The shallots should be soft and tender.
Add the sliced mushrooms, lemongrass, dry vermouth and white wine and bring up to the boil.
Boil the wine for 2-3 minutes then add the milk.
Return to the boil then reduce the heat and simmer for 2-3 minutes.
Take the sauce off the heat, cover with a lid and allow to infuse for two minutes.
Strain the sauce through a fine sieve into a clean pan.
Using a stick blender slowly incorporate a pinch more salt, the wasabi paste, lemon juice and xanthan gum or arrowroot to the sauce.
For the fregola, bring a large pan of heavily salted water to the boil, add the fregola and cook for 4-5 minutes. The texture is similar to pasta and will have a slight bite.
Drain the fregola and return to the saucepan. Add the lemon juice, olive oil, chopped scallops, herbs and half the sea vegetables to the fregola and mix together.
Cover with cling film to keep warm until ready to serve.
For the scallops, heat a little olive oil in a non-stick pan over a medium heat. Season the scallops with salt before placing into the pan and cook until golden-brown, about 1-2 minutes.
Turn the scallops over and turn off the heat. Allow to rest in the pan for a further minute or so, depending on their size, until just cooked.
Place onto a tray lined with kitchen paper then squeeze over the lemon juice.
To serve, place 2-3 tablespoons of fregola in the bottom of each of four serving bowls.
Place three roasted scallops on top of the fregola.
Arrange the sea vegetables and the fresh dulse (söl) around the scallops.
Pour over the hot wasabi sauce and sprinkle over dried dulse (söl) powder.


{% extends "base.html" %}
{% block navbar %}
{% include 'navigation_instructor_dashboard.html' %}
{% endblock %}
{% load crispy_forms_tags %}
{% block content %}
Your email address and GitHub id can not be updated here, as they are tied to your login id. Please contact us at<EMAIL_ADDRESS>if you would like to update this information.


At Good, Form & Spectacle we're interested in what happens when you take away search boxes on websites. Turns out it can be pretty fun, especially when you have well-constructed classifications that lead you to movies you didn't know you wanted to watch.
Here's the story of our Netflix Scrambler...
Alexis Madrigal tweets his curiosity about Netflix "microgenres".
And lists them in a Google doc.
Noodling around one evening, I discover a magic URL thanks to Mr. Madrigal's sleuthing. If you're a member on Netflix, you can bounce around genres just by changing the number part of the URL...
Within moments, Paul Mison and Nick Sweeney join in and start to try to gather genres.
Madrigal publishes in The Atlantic. How Netflix Reverse Engineered Hollywood included a "Netflix genre generator" and lots of interesting stuff about how he pulled everything apart.
"We've now spent several weeks understanding, analyzing, and reverse-engineering how Netflix's vocabulary and grammar work. We've broken down its most popular descriptions, and counted its most popular actors and directors.
"To my (and Netflix's) knowledge, no one outside the company has ever assembled this data before.
"What emerged from the work is this conclusion: Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented. The genres that I scraped and that we caricature above are just the surface manifestation of this deeper database."
So, before we knew it, Madrigal's research had become a full-blown article. Darn! I was left feeling that this "caricature" in the article was just that, and not actually useful at all to people looking for something to watch. Could we make something useful? My interest is not dismemberment, but revelation and fun.
Since the article came out, we've each looked at this when we've had time. There's a server and search, a lot of poking at big spreadsheets, and getting together when we could. Designs started popping out mid-September, and here we are.
We hope you enjoy it! Scramble?


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WITNESS LIST State, Federal & International Relations Committee April 3, 2001-8:00A HB 2360 For: Manning, Greg (Sons of Confederate Veterans, Texas Division) Registering, but not testifying: For: von Roeder, Steve (Self) Davis, Marshall (Self) Irby, H.W. (Self) Jones, Rob (Self and Texas Heritage Coalition) Patterson, Jerry (Self) Sweeney, Denne (Self) Weber, David (Self) HB 3428 On: Stanford, John (The Adjutant General's Department) HB 3589 For: Adair, Dwight (TX Film Industry, Granite House, Inc.) Lipman, Terence (TX Film Industry, TX Film Development Project) On: McNeil, Dan (TX Comptroller's Office)


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News Fact Checker
About
People contributed to this project:
Brent Biglin
Anubrata Das
Aditya Kharosekar
Saumyaa Krishnan
Siddhesh Krishnan
Matthew Lease
ChiaHui Liu
An Thanh Nguyen
Shravan Ravi
Alex Swearingen
Elizabeth Tate
Jyothi Keshavan Vinjumur
Byron Wallace
Anubrata Das
Aditya Kharosekar
Saumyaa Krishnan
Siddhesh Krishnan
Matthew Lease
ChiaHui Liu
An Thanh Nguyen
Shravan Ravi
Alex Swearingen
Elizabeth Tate
Jyothi Keshavan Vinjumur
Byron Wallace
The following paper describes the method:
An Thanh Nguyen, Aditya Kharosekar, Matthew Lease, and Byron C. Wallace.
An Interpretable Joint Graphical Model for Fact-Checking from Crowds
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-18)
An Interpretable Joint Graphical Model for Fact-Checking from Crowds
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-18)


I consider myself the best type of candidate, one disappointed in things around me and looking to improve the world. I know technology is the best way to go about it due to the nature of its implementation, it can affect a city or entire nations with just keystrokes. I would like my skills to mean something, to collaborate with people I can learn from and teach; to work on problems and solve them.
I worked nights at a supermarket and attended school in the morning for most of my degree. I have been a technology enthusiast since I was a child, and I have always been fascinated by how technology can be used to improve people's lives. I am confident that my skills and experience make me a strong candidate for any technical position.
Android Development
Git Version Control
Swift Development
Linux Development
Bootstrap
Mac OS Development
C++
Javascript
Java
mysql
PHP
Windows Server
python
C#
Visual Stuido
Windows User and Admin Environment
HTML5
Illustrator
Photoshop


| vec_equal {vctrs} | R Documentation |
vec_equal_na()
tests a special case: equality with NA
. It is similar to
is.na but:
Considers the missing element of a list to be NULL
.
Considered data frames and records to be missing if every component
is missing.
This preserves the invariant that vec_equal_na(x)
is equal to
vec_equal(x, vec_init(x), na_equal = TRUE)
.
vec_equal(x, y, na_equal = FALSE, .ptype = NULL) vec_equal_na(x)
x |
Vectors with compatible types and lengths. |
y |
Vectors with compatible types and lengths. |
na_equal |
Should |
.ptype |
Override to optionally specify common type |
A logical vector the same size as. Will only contain NA
s if na_equal
is FALSE
.
vec_cast_common()
with fallback
vec_equal(c(TRUE, FALSE, NA), FALSE) vec_equal(c(TRUE, FALSE, NA), FALSE, na_equal = TRUE) vec_equal_na(c(TRUE, FALSE, NA)) vec_equal(5, 1:10) vec_equal("d", letters[1:10]) df <- data.frame(x = c(1, 1, 2, 1, NA), y = c(1, 2, 1, NA, NA)) vec_equal(df, data.frame(x = 1, y = 2)) vec_equal_na(df)


Picturesque.ai is made by a group of photo lovers and AI enthusiasts. As a photo recommendation application, picturesque.ai will help you choose photos that are more aesthetically pleasing to viewers using state-of-the-art deep learning technique.
Here is how it works
Step 1 Upload up to 5 photos.
Step 2 Submit.
Step 3 Results.


3 writes to CurrentPipelineObject
System.Management.Automation (3)
engine\CommandProcessor.cs (2)
517
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663
Command.
CurrentPipelineObject
= inputToOperateOn;
engine\CommandProcessorBase.cs (1)
722
Command.
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9 references to CurrentPipelineObject
System.Management.Automation (9)
engine\NativeCommandProcessor.cs (1)
438
_inputWriter.Add(Command.
CurrentPipelineObject
);
engine\ParameterBinderBase.cs (2)
1049
currentValue == _command.
CurrentPipelineObject
.BaseObject)
1051
currentValue = _command.
CurrentPipelineObject
;
engine\runtime\CompiledScriptBlock.cs (2)
2258
if (
CurrentPipelineObject
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2264
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engine\ScriptCommandProcessor.cs (4)
389
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420
_input.Add(Command.
CurrentPipelineObject
);
447
_input.Add(Command.
CurrentPipelineObject
);
451
RunClause(processBlock, Command.
CurrentPipelineObject
, _input);


Part 1 | Part 2
“Man, your code reviews are brutal!” says John to your teammates. He’s talking about you.
“Why do you hate me so much?” writes Steve in an email to you.
Sound familiar? You might just be Seth, one of the people most familiar with the project and gatekeeper of the code base. You’re not out to write “brutal” reviews, or make your colleagues hate you. You just want to ensure that only the highest quality code makes it into production, as bug free as possible.
So why do your coworkers think you hate them?
But that’s not really how you intended to come off, or you likely wouldn’t be reading this.
To try to understand more of John and Steves’ perspectives, read Part 1 of this article.
So why should you even care? Can’t John and Steve just grow a thick skin? Why can’t they just stop whining?
Because they’re human, and we all have extremely varying experiences, all of which drive how we interpret the behaviour of others. Some people will assign a benevolent tone, while others will just as easily assign a malevolent one.
When John and Steve interpret your comments negatively, their performance will decline.  They will not be able to thrive in such an environment, and your overall team performance will suffer.
Fortunately, you can fix it.  Their morale will increase, and their productivity with it.
It all starts with having the right tone. Without proper use of tone, it’s very easy to to lose the meaning behind the message.
When tone isn’t explicit, it’s easy to interpret it negatively. It’s also highly individual: John may read a comment as a friendly suggestion, while Steve reads it as an attack on his intelligence.
Writing comments without paying attention to tone is a default mode for most people. You must consciously insert tone when writing in order to prevent John and Steve from attributing a negative tone.
Fortunately, it’s a lot easier to fix than you would think. With some practice it will become more natural, and eventually you will be inserting explicit tone automatically.
The following techniques aren’t just applicable to just code reviews, but to all text-based communication you’ll use in daily life: emails, texting, chat, etc. Follow these suggestions and John and Steve will no longer think of you as a brutal gatekeeper:
1. You must focus on making your tone friendlier.
You can use the passive voice to remove a subject from the sentence, placing emphasis on the outcome of the action. This focuses on the problem, and not the person. Instead of “You must use four spaces for indentation”, use “Four spaces must be used for indentation.” Notice how the “you” has been completely removed from the sentence.
Example
- Bad: Kill this.
- Good: Er, was this accidentally committed?
You can also use the collective “we” in place of “you”. For example, instead of “You must always re-throw exceptions here”, use “We must always re-throw exceptions here.” This de-emphasizes the original submitter as the target of the comment, and re-emphasizes that this is a team effort.
Example
- Bad: Can’t you just do it this other way?
- Good: Unfortunately we need to it this particular way, or else the whizbob will become unstable and raise exceptions.
Use the passive voice, the collective “we”, and focus on the problem, not the person.
2. Ask questions.
Asking questions is also a great way to bring up an issue. They become responsible for the answer, and this gives them a chance to explain themselves. For example: Instead of “this will fail”, try “What happens if the gidget is set to 42?” It could be that they have a legitimate reason for submitting the code as it is.
Example
- Bad: You can’t do that.
- Good: Are you sure that this is going to work? What happens if chunky_bacon is set to null?
3. Build good rapport with your teammates and they will integrate that into your tone.
Additionally, build rapport with the submitter. Eat lunch with them, play games with them (in person or online), get to know them. If this is something that is difficult for you, begin with a casual “got any plans for the weekend?” Show some interest in their response. If they answer with “I did some karate”, ask them about it. “Oh cool! How long have you been doing karate for? What do you enjoy about it?” Keep the questions focused on them, and don’t bring the conversation back to you. Bad example: “Oh cool! I’ve always wanted to do karate.” This brings the conversation back to you, and gives them the opportunity to lose interest.
Additional strategies you can use:
Avoid imperative forms. Make suggestions, not commands. Instead of “do it this way”, try “it might work better if we did it this way.” If you issue commands you will come across as demanding.
Prefer an informal tone over a formal tone. Instead of “This could be null here.”, try “this could be null here”, noting the lack of punctuation. Punctuation is still important, but the use of initial capitals and final periods can be safely discarded.
Don’t end your statements with a period. Studies have shown that ending your sentences with a period makes them come across as less sincere.
Use liberal use of emoticons and emoji. They explicitly demonstrate tone :) If you’re worried that the receiver will be annoyed by them, pay attention to how they themselves write, and tailor your level of emoticons to match theirs.
Don’t be terse. Terseness comes across as impatient and frustrated. Instead of “don’t do this”, prefer “if we do it this way, these bad things could happen”, or even better: “can you foresee any bad things happening if we do it this way?”
Admit that you may not understand everything. Instead of “don’t do this”, try “I don’t understand this. Would you be able to elaborate?”
Have clear guidelines and coding styles so that they don’t turn into an issue during reviews. Have an automated tool to check those styles before code is even committed.
Make it explicit when a comment is a suggestion: “it might work better if this implementation had rockets”.
Make it explicit when a comment is a mandatory change: “unfortunately we must transmit the the signal here, or the aliens will die”
Add compliments, acknowledgement, and gratitude: “Thanks, this looks awesome! Just a few things that I think we should change.”
Don’t write in anger.  Take a deep breath, step away from the computer for a few minutes. Calm down, come back, and write “I’m not sure this will run without throwing an error.” Otherwise you risk building resentment in the receiver.
Still worried that your colleagues might be interpreting your comments negatively? Grab them and go over your code review together, whether it’s in person or using Screenhero. Then you can explicitly add your tone in your voice.
With these guidelines, you’ll start to find that John and Steve are friendlier towards you, and their productivity will increase. These techniques will help you across all written communication, not just for code reviews. You will become a more pleasant person to work with, and that alone makes it worth the effort.


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Share Your World!
Set your default map location, configure your preferences,
and tell us about yourself.
Zoom anywhere in the world and print a paper map of your area of interest. You can even create a worksheet to help with data collection.
Once you've printed your map, you can write on it using a Colored Pen, mapimage or take a photograph of it, and upload it back to the website.
You can also add other media to your map (things like audio recordings or photos). Just click the Upload button and drag media onto the web page.
Once you've submitted all of your data / media, you can edit and configure your maps, to visualize your data and analyze it.
And now that you've created a unique and beautiful map, why not share it? You can email your map to others or even download your data again.


I am experienced in leveraging linguistic elements to provide excellent quality work. I have experience in the field of localization, machine translation, and computer aided translation. I have also leveraged my skills to provide excellent machine trainable data solutions like, transcription, annotation, tagging, and creating language model.
Transcribing YouTube audios, segementing, and annotating them. I have used Google's proprietary software to transcribe the audios leveraging my linguistic knowlegde.
Reviewing translated content for their quality, readability, meaning,and compliance-related issues across various monetization,marketing, UI, products. Performed quality checks of the various content. I also translated and edited the content as and when it was required and provided feedback and support for linguistic tools optimization.
Rated Facebook ads for their relevance and appropriateness using Facebook's rating tool to improve the accuracy of the targeted campaigns.
Worked on a project related to Hindi Sentiment annotation and also reviewed/audited pre-annotated corpus for Appen. Sentiment was to understand and classify the user's reaction into broad categories of happy, sad, disgust, and angry.
Created grammar for Speech Recognition Systems using Speech Recognition Grammar Specification (SRGS) in grxml for Nuance Communcations wherein I developed the grammar to recognise expressions like date, time and greeting expressions.
This grammar is used by speech recognition devices like Google Home and Alexa to understand these type of expressions from the user and provides the reply. I also added and edited the model to adapt and to scale it to the Hindi speaking community.
I was involved in leveraging my linguistic knowlegde to tag Named Entities in order to build and effective NER (Named Entity Recognition) tagger and POS (Part of Speech) tagger for Hindi Language.
I was involved in leveraging my linguistic knowlegde to provide a linguistic analysis of the tribal language 'Koyi'. The intership was on tribal livelihood, Language and Education.
I taught English and Computer Science to visually impaired children at this NGO.
 I also love playing Cricket and Football.
I play multiplayer online games occasionally, I follow a number of sci-fi and fantasy genre movies and television shows.


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|
home | news | discuss | issues | license LURE: random |
|
|---|---|
Random number<EMAIL_ADDRESS>Lua's Can't have that: tests won't port across platforms. The following library is recommended as a replacement for the ANSI C rand() and srand() functions, particularly in simulation applications where the statistical 'goodness' of the random number generator is important. The generator used in this library is a so-called 'Lehmer random number generator' which returns a pseudo-random number uniformly distributed 0.0 and 1.0. The period is (m - 1) where m = 2,147,483,647 and the smallest and largest possible values are (1 / m) and 1 - (1 / m) respectively. For more details see:
Also, the raw random number generator is wrapped in a 97 table to increase randomness. Typical usage:
Magic numbers |
local seed0 = 10013
local seed = seed0
local multipler = 16807.0
local modulus =<PHONE_NUMBER>.0
local randomtable = nil
|
|
New seed is calulated from the old seed. New random number is calculated from the new seed. Cycle= 2,147,483,646 numbers |
local function park_miller_randomizer()
seed = (multipler * seed) % modulus
return seed / modulus end
|
|
Set the random number seed |
local function rseed(n)
if n then seed = n else seed = seed0 end
randomtable = nil end
|
|
Set the random number seed using Lua stuff (note: will generate different numbers on different platforms.) |
local function system()
return rseed(math.random()*modulus) end
|
|
Main driver |
local function another ( x,i)
if randomtable == nil then
randomtable = {}
for i = 1, 97 do
randomtable[i] = park_miller_randomizer() end end
x = park_miller_randomizer()
i = 1 + math.floor(97*x)
x, randomtable[i] = randomtable[i], x
return x end
|
|
Public interface |
return {seed=rseed, system=system,r=another}
|
LegalLURE, Copyright (c) 2017, Tim Menzies All rights reserved, BSD 3-Clause License Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
|


we made pants , mine are purple with white polka dots, we started by cutting a piece of fabric with a stencil and making the waist tie before sewing them. The hardest part about this process was sewing in a straight line and making it have a nice appearance I learned a lot about pants and making seams.


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Makes 28 sausage rolls, 20 palmiers, 24 cheese straws
These three recipes work really well together and should provide something for everyone. The best thing about this trio is that they can be prepared well in advance and can all be cooked together in the same oven at the same time for a Christmas party.
For the chorizo sausage rolls, melt the butter in a frying pan over a medium heat, add the onion and thyme and cook gently for five minutes, or until softened. Add the paprika and cook for a further minute. Remove from the heat and leave to cool.
Remove the skins from the chorizo and sausages and place in a mixing bowl, add the cooled onions and mix well until completely combined. Unroll the pastry out on a lightly floured surface into a large rectangle and then cut lengthways into two long strips. Divide the sausage mixture in half and roll into two long sausages roughly the same length as the pastry.
Brush the pastry with a little beaten egg, lay a sausage down one side of a pastry strip, and roll up tightly. Seal the edge. Repeat with the remaining pastry and filling, then transfer to a tray join-side down, brush all over with the beaten egg and refrigerate, covered, for 30 minutes.
Preheat the oven to 200C/400F/Gas 6 (180C fan). Cut each roll into 14 equal sized slices and lay on baking trays. Bake for 25 minutes, or until crisp and golden-brown. Serve warm.
For the tapenade and parmesan palmiers, lightly flour a clean work surface. Unroll the pastry out onto the floured surface into a large rectangle.
Spread the tapenade over the pastry in an even layer, spreading to the edges of the long end and about 2cm/¾in from the edges of the short end. Roll the two long sides up tightly to meet in the middle, turn over, cut into 1cm/½in wide palmiers, place cut-side down on a baking tray and press lightly to flatten slightly. Brush all over (including the sides) with a little beaten egg and sprinkle over the parmesan. Refrigerate for 30 minutes.
Cook the palmiers at the same temperature as the sausage rolls. Bake for 12-15 minutes, or until crisp and golden-brown. Serve warm. Any leftover tapenade can be used as a dip.
For the cheese straws, unroll the pastry onto a lightly floured surface into a large rectangle. Sprinkle half of the cheese and all of the cayenne pepper over one half of the pastry and then fold over to seal.
Using a rolling pin, roll the pastry into a large rectangle, no thicker than a pound coin. Brush with the beaten egg and then sprinkle over the remaining cheese. Cut in half lengthways and then cut each piece into 12 equal strips. Twist each strip into a neat spiral and lay on a baking tray. Cover with cling film and refrigerate for 30 minutes.
Line two baking trays with greaseproof paper. Remove the cling film from the cheese straws and lay them on the lined baking trays. Bake the cheese straws at the same oven temperature as before for 6-8 minutes, or until crisp and golden-brown. Serve warm.


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class Solution: def closestCost(self, bc: List[int], tc: List[int], target: int) -> int: ret, diff = float("inf"), float("inf") def helper(p, i): nonlocal ret, diff if p > target and p - target >= diff: return if i == len(tc): if abs(target - p) < diff: #print(p, i) diff = abs(target - p) ret = p return helper(p, i + 1) helper(p + tc[i], i + 1) helper(p + tc[i] * 2, i + 1) for c in bc: helper(c, 0) #print("%%", ret, diff) return ret


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{% set suppress_feedback = true %} {% extends '_layouts/layout.html' %} {% block page_title %}{% endblock %} {% block body_css_classes %}nhsuk-frontend{% endblock %} {% block content %}
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You can read more stay at home advice on NHS.UK.
If you feel you can’t cope with your symptoms at home or your condition gets worse use the 111 online service again.
You should remain at home until 7 days after your symptoms started.
After 7 days, if you feel better and no longer have a high temperature, you can go back to doing what you would normally do.
The cough may go on for some weeks in some people, even though the coronavirus infection has cleared. A cough by itself does not mean you have to stay at home for more than 7 days.
If you do not get better after 7 days use the 111 online assessment service again before you leave your home or let visitors in.


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Event Horizon (1997)
A movie review by Michael J. Legeros Copyright 1997 by Michael J. Legeros
(Paramount) Directed by Paul Anderson Written by Philip Eisner Cast Laurence Fishburne, Sam Neill, Kathleen Quinlan, Joely Richardson, Richard T. Jones, Jack Noseworthy, Jason Isaacs, Sean Pertwee MPAA Rating "R" (presumably for gore and violence) Running Time 97 minutes Reviewed at Mission Valley Cinemas, Raleigh, NC (16AUG97)
==
The title refers to the name of a derelict spaceship that is dis- covered in 2047, some years after it disappeared on its maiden voyage. Laurence Fishburne, Kathleen Quinlan, Richard T. Jones, Joely Richard- son, Jack Noseworthy, Jason Isaacs, Sean Pertwee, and Sam Neill play the search and rescue team that investigates the vessel, and to whom bad things begin to happen. Without revealing too many plot particulars, think of EVENT HORIZON as a big boo-house with some splatter movie tendencies. (ALIEN meets HELLRAISER, if you will.) Jolts a-plenty (strobes, sound effects, dream sequences, etc.) plus buckets of blood, ranging from clawed-out eyes to the gleefully grisly sight of a fully disemboweled human body. (You'll laugh, you'll heave.) The art direction and special effects are breathtaking and certainly a world more impressive than anything having to do with plot, dialogue, or character. (You *do* get to see the ship's navigator, the lovely Ms. Richardson, emerge from a "gravity immersion tank" wearing nothing but shorts and a jogging bra. Does that count as character development?) Directed by Paul Anderson (MORTAL KOMBAT), who pulls one or two memor- able sequences out of his butt: a hull breach, the restoration of gravity in zero-G, and a very cool example of how to rescue someone who isn't wearing a space suit when the airlock opens. Magnetic boots on.
Grade: C-
-- Mike Legeros - Movie Hell http://www.nonvirtual.com/hell
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