I'm new to programming in swift code and was hoping someone could help me with my project.
I would like to create an activity tracker that logs a users time and distance. Overall there is a total (say 200,000km) from London to Australia to achieve, routing through a few countries along the way.
Each time a user records an activity the total is reduced by the amount the user has recorded until the total is zero.
Secondly, I would like to show the overall progress on a map and record the progress from start.
I started by following a tutorial online that seems to be a good start. But I don't know how to log distance and time, and then do the bits above
func locationManager(_ manager: CLLocationManager, didUpdateLocations locations: [CLLocation])
{
let location = locations[0]
let span:MKCoordinateSpan = MKCoordinateSpanMake(0.01, 0.01)
let myLocation:CLLocationCoordinate2D = CLLocationCoordinate2DMake(location.coordinate.latitude, location.coordinate.longitude)
let region:MKCoordinateRegion = MKCoordinateRegionMake(myLocation, span)
Map.setRegion(region, animated: true)
self.Map.showsUserLocation = true
}
Any help would be greatly appreciated.
Kind Regards,
Have you contemplated using a database to store the data?
The tree would have a branch for each user with 3 sub-branches (Time, Distance, DistanceLeft)
Then once the users distance is updated simply do the algorithm the return the distanceLeft and show that to the user
You can also use MKMap Annotations to log the distance traveled
Firebase would be a great place to start to store and log your data.
Related
I am trying to search for suggestions and solutions, but I am unable to find any.
After reading blogs, I am able to build a time series anomaly detection using BigQuery ML (Arima Plus).
My question is: how do I put such a model in production?
Probably I need to:
program the re-training of the model every X days
check whether there are new anomalies on the object table every X hours
record those anomalies in another table
But I also accept other suggestion on how to proceed.
Is there anyone out there that can give me any hint?
Thank you!
The best way I found is to create "scheduled queries":
schedule a query for re-training of the model every X days:
CREATE OR REPLACE MODEL mymodel
OPTIONS( model_type='arima_plus',
TIME_SERIES_DATA_COL='events',
TIME_SERIES_TIMESTAMP_COL='approx_hour',
HOLIDAY_REGION = 'GLOBAL',
CLEAN_SPIKES_AND_DIPS = FALSE,
DECOMPOSE_TIME_SERIES=TRUE)
AS (SELECT
TIMESTAMP_TRUNC( PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%E*SZ',start_time), hour) as approx_hour,
COUNT(1) AS events
FROM `mytable`
GROUP BY approx_hour);
schedule a query to perform anomaly detection on the latest events, and eventually write them on a table:
insert into `events_anomalies_table`
SELECT approx_hour as hour,
cast(events as int64) as actual_events,
cast(lower_bound as int64) as expected_min_events,
cast(upper_bound as int64) as expected_max_events,
current_timestamp() as execution_timestamp
FROM ML.DETECT_ANOMALIES(
MODEL`my_model`,
STRUCT (0.98 AS anomaly_prob_threshold),
( SELECT
TIMESTAMP_TRUNC( PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%E*SZ',start_time), hour) as approx_hour,
COUNT(1) AS events
FROM `my_table`
WHERE PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%E*SZ',start_time) > TIMESTAMP_SUB(CURRENT_TIMESTAMP() , INTERVAL 1 HOUR)
GROUP BY approx_hour
LIMIT 1))
WHERE is_anomaly = True
While training my predictor I came across this error and I got stuck how to fix it.
I have two data-series, a "Target time-series data" with 9234 rows and a single "item_id" and a second one that is "Related time-series data" with the same number of rows as I only have a single id.
I'm setting de data with a window of 180 days, what is exactly the difference between the second and the first number that has appeared on the error, 9414 - 9234 = 180.
We were unable to train your predictor.
Please ensure there are no missing values for any items in the related time series, All items need data until 2020-03-15 00:00:00.0. For example, following items have missing data: item: brl only has 9234/9414 required datapoints starting 1994-06-07 00:00:00.0, please refer to documentation for additional details.
Once my data don't have missing data and it's on a daily basis why is it returning this error?
My data starts on 1994-06-07 and ends on 2019-09-17. Why should I have 9414 data points rather than 9234?
Should I take out 180 days in my "Target time-series data"?
The future values of the related time-series data must be known.
Example of a good related-time series: You know past and future days in which marketing has or will send email newsletters promoting the product you're forecasting. You can use this data as a related-time series.
Example of a bad related-time series: You notice that Google searches for your brand correlated with the sale of your product. As a result you want to use it as a related-time series. Since you don't know how many searches will occur in the future, so you can't use this as a related time series.
In you case, You have TARGET_TIME_SERIES data for 9414 days and you want to predict demand for the next 180 days. That means your RELATED_TIME_SERIES data should be 9594 days.
Edit: I have not tested this with amazon's forecasting product. I'm basing my answer on working with Facebook Prophet (which is one of the models amazon forcast uses). Please let me know if my solution worked.
Is there a way to get all neighborhoods per city by lat and lng from mapbox API V5.
For example, if I search using the lat and lng of Long Beach.
-118.1937, 33.7701
I expect to get back all the neighborhoods, instead, I only get back one result of
"place_name: "Downtown, Long Beach, California 90802, United States""
I have changed the response limit and bound box, with no results.
Here is the mapbox playground.
https://www.mapbox.com/api-playground/#/forward-geocoding
Thanks!
Mapbox doesn't really do neighborhoods, they require some sort of search data to pull either addresses or places.
However, there are services where you can get neighborhood data. I found this Stack Overflow question to have several links (sadly, most of them outdated....), with the reference to Zillow having a lot of promise.
I'd also suggest the Census Bureau data as it may have what you are looking for, but it is what I would call 'less than user friendly' to find anything - unless you are comfortable reading government spec sort of things... :)
I have var source="<p><img src="http://l.yimg.com/bt/api/res/1.2/TRLtYhdbTvFcX_GOU_0S4g--/YXBwaWQ9eW5ld3M7Zmk9ZmlsbDtoPTg2O3E9ODU7dz0xMzA-/http://media.zenfs.com/en_us/News/Reuters/2012-04-14T023232Z_5_CBRE83B1MAL00_RTROPTP_2_USA.JPG" width="130" height="86" alt="People visit Google's stand at the National Retail Federation Annual Convention and Expo in New York" align="left" title="People visit Google's stand at the National Retail Federation Annual Convention and Expo in New York" border="0" />(Reuters) - An unusual stock split designed to preserve Google Inc founders' control of the Web search leader raised questions and some grumbling on Wall Street, even as investors focused on the company's short-term business concerns. Shares of Google closed 4 percent lower at $624.60 on Friday, driven by deepening worries about its search ad rates and payments to partners. The declining search trends underscored investor uncertainty about Google's growth prospects and unease about the company's pending $12.5 billion acquisition of Motorola Mobility. ...</p><br clear="all"/>" Now i need to parse/scrape this to get the link address in a variable i.e http://in.news.yahoo.com/googles-stock-split-raises-questions-023232813.html and the image src in a separate variable. I also need the description text between </a> and </p>.. Kindly help i am badly stuck...
Try the below code snippet using HtmlAgilityPack
var source = #"<p><img src=""http://l.yimg.com/bt/api/res/1.2/TRLtYhdbTvFcX_GOU_0S4g--/YXBwaWQ9eW5ld3M7Zmk9ZmlsbDtoPTg2O3E9ODU7dz0xMzA-/http://media.zenfs.com/en_us/News/Reuters/2012-04-14T023232Z_5_CBRE83B1MAL00_RTROPTP_2_USA.JPG"" width=""130"" height=""86"" alt=""People visit Google's stand at the National Retail Federation Annual Convention and Expo in New York"" align=""left"" title=""People visit Google's stand at the National Retail Federation Annual Convention and Expo in New York"" border=""0"" />(Reuters) - An unusual stock split designed to preserve Google Inc founders' control of the Web search leader raised questions and some grumbling on Wall Street, even as investors focused on the company's short-term business concerns. Shares of Google closed 4 percent lower at $624.60 on Friday, driven by deepening worries about its search ad rates and payments to partners. The declining search trends underscored investor uncertainty about Google's growth prospects and unease about the company's pending $12.5 billion acquisition of Motorola Mobility. ...</p><br clear=""all""/>";
HtmlDocument doc = new HtmlDocument();
doc.LoadHtml(source);
var paraNode = doc.DocumentNode.SelectSingleNode("//p");
var desc = paraNode.InnerText;
var anchorNode = doc.DocumentNode.SelectSingleNode("//p/a");
var link = anchorNode.GetAttributeValue("href", null);
var imgNode = doc.DocumentNode.SelectSingleNode("//p/a/img");
var src = imgNode.GetAttributeValue("src", null);
There are many ways to do this, but this is just one of the approach to get the job done. It gives you an idea how to do it with HtmlAgilityPack. XPATH will give you lot of power while parsing stuff like this.
Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
User order history
Product pageview history for both anonymous and registered users
All of this data is timestamped.
What I'm looking for
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Any additional data I can collect for a user that can directly imply an interest in a product
Algorithms/equations for turning this data into scores for each product
What I'm NOT looking for
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Generating a number of user clusters (21 at the moment) using the k-means clustering algorithm, using the pearsons coefficient for the distance score
For each user (on demand) calculating their a graph of similar users by looking for their most and least similar users within their cluster, and repeating for an arbitrary depth.
Calculating a score for each product based on the preferences of other users within the user's graph
Sorting the scores to return a list of recommendations
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Here's a haymaker of a response:
time spent looking at a product
semantic interpretation of comments left about the product
make a discussion page about a product, brand, or product category and semantically interpret the comments
if they Shared a product page (email, del.icio.us, etc.)
browser (mobile might make them spend less time on the page vis-à-vis laptop while indicating great interest) and connection speed (affects amt. of time spent on the page)
facebook profile similarity
heatmap data (e.g. à la kissmetrics)
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both #Andrew Ingram and anyone else who has the same question and found this thread through search.)
You can allow users to explicitly state their preferences, the way netflix allows users to assign stars.
You can assign a positive numeric value for all the stuff they bought, since you say you do have their purchase history. Assign zero for stuff they didn't buy
You could do some sort of weighted value for stuff they bought, adjusted for what's popular. (if nearly everybody bought a product, it doesn't tell you much about a person that they also bought it) See "term frequency–inverse document frequency"
You could also assign some lesser numeric value for items that users looked at but did not buy.