I'm attempting to set up an OpenCart store for a client.
I'm getting the following error on the shipping page.
"Warning: No Shipping options are available. Please contact us for assistance!"
Research suggests that this error happens when there is a mismatch between the weight-class for the store and for the plugin, or something similar.
I've tried every combination of configuration settings that I can think of without result.
I'm not familiar enough with OpenCart to debug this issue. Where do I need to start looking?
Firstly you have to enable the shipping status and the values from admin panel shipping tab.After that you can get it in the front end.
My troubleshooting procedure:
The store weight UOM had been set to ounces.
The Fedex plugin doesn't support ounces as a weight UOM.
Nothing works.
The store weight UOM was changed to LBS.
Nothing works.
The package size was set to FedEx 10 KG Box
The Fedex plugin can't convert from Lbs to KG on the fly.
The package size was changed to "Fedex Box", without a weight class
Some products now working, all shipping estimates are WAY high.
When changing the default UOM for the store, no existing weights are converted in to the new units, although any weights stored without a unit are now read as being in the new unit.
This meant that the fedex system was trying to pull prices for items that "weighed" hundred of ounces (which it should have been able to do, even though those weights were incorrect.)
I updated the weights on all products to be in line with their unit of measure
At this point, the plugin was working for most, but not all, products, with reasonable accuracy.
I changed the plugin settings from List Rate to Account rate.
Now everything works.
To simplify - The fedex shipping plugin in opencart 1.5.6 will only work if:
All the products in the system have their weights stored in the same UOM.
That UOM is either pounds or kilograms (not ounces!)
A geozone is set, and a zip code is supplied(zip codes are important!)
The package size matches the unit of measure for the weight (no mixing kilograms and pounds!)
The product weights are actually correct
The account in question has a rate for a package of the indicated size
Hopefully someone else will find this helpful.
Ive been through this problem as well and I haven't yet fixed it completely.
But in my particular problem, I had a syntax error in the XML retrieved by the Fedex server after the cURL request.
Printing the $response variable I could find some good hints about some of the problems, for example, commas (,) instead of dots (.) to refer to decimal numbers and decimal numbers where it was expecting an integer.
So var_dump($response) could help some people find their specific issues.
Related
Firstly apologies if this has been answered elsewhere.
I am using QuantLib (via Excel) to build a "standard" bond pricing sheet: prices, yields, spline AND matched-maturity ASW.
I can price the bonds, and have successfully built a forecast (Euribor) and discount (EONIA) curve. I can use qlMakeVanillaSwap() to define a spot-start swap by tenor (eg "1y","2Y" etc) and it works fine. However I am struggling to define a "broken date" swap, ie one which starts T+2 and ends on a given date (and so usually has a short stub on the first payment), to match the bond maturity. All the examples I can find have integer year tenors.
I would be grateful if someone could point me to the right method (can be in python, C++ or Excel). Or do I have to go down the route of creating explicit fixed and floating rate schedules for the swaps?
The answer seems to be: Yes, I do have to create explicit fixed and floating rate schedules, using qlSchedule(), but it turns out to be not too onerous. NB. I am pricing a vanilla EUR ABB vs 6m Euribor swap.
As for pricing, it seems the qlMakeVanillaSwap() is doing a few helpful things in one call, but only IF your swap has a whole-period tenor (eg "1y"). I found the answer for what I wanted to do in the example sheet that came with the QuantLibXL download package.
The other thing that qlMakeVanillaSwap() is doing (in addition to creating the schedules) is setting the Pricing Engine (which is used to discount the cashflows). In the longer version you have to (a) set it yourself using qlInstrumentSetPricingEngine() and (b) pass the result of that call to the Trigger parameter of qlVanillaSwapFairRate(), to establish the calculation order.
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 am on design number three I think now of a program that submits a series of stock tickers and metrics to Yahoo Finance. I don't need to go into too much total about what it does as I have got most of it up and running now apart from one remaining issue.
The Yahoo Finance site lists about 2700 stock tickers on the NASDAQ alone. I anticipated that submitting all of these in one filename URL statement might fall over for some reason, so set an initial string length of 500 tickers and built some nested macros to iterate through in 500 ticker blocks until everything I wanted had been extracted.
However during development of the code it seems that if I build a string with any more than about 200 tickers in I get an error telling me that SSL Support cannot be run and the code falls over.
Does anyone have any idea why this is? In ideal world I would like to be able to do this code in one pass where all 2700 stock tickers are pulled down. If this isn't possible if someone could explain why not that would be great.
Thanks
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.