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I am trying to create a workaround on our website. I need to say if the shipping charge from UPS + the oversized charge we enter per product equal less than 1 (0 or a negative number) the shipping charge should be 0.
Trying to create a work around to do free shipping by product.
The problem is I don't know ColdFusion. Our system does not have an option for free shipping on the product level. I tested adding a negative amount in the field for upcharge on oversized items. This worked except that if the shipping that was returned was negative it deducted that amount from the sale. Example widget $10 shipping $8.50 and oversized charge was set at -10 the sales was for $8.50. I need to set the negative amount high enough that it covers the range of ground shipping charges, so I need to code the charge can never be less than 0. Hope this makes more sense.
Thanks
You could try
max( 0, (upsCharge + oversizedCharge) )
Not sure what you are asking. How to write the if statement in ColdFusion?
<cfif (UPSCharge + OversizeCharge) GT 0>
<!--- add shipping charge --->
<cfelse>
<!--- free shipping --->
</cfif>
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The input is a person's date of birth (e.g. 22/Feb/78).
Using a regular expression, I want to find out if the person has their birthday within the next two weeks from now.
So I want to know if February 22nd (the year needs to be ignored, of course) is within the next 14 days from now. Today is February 13th, so the correct result would be: yes.
Is there a way to do this?
I tried ChatGPT but it was not available for me due to capacity reasons.
So I tried https://www.autoregex.xyz/ and entered "Is the date (mm.dd.) within the next two weeks?".
Result:
\d{2}\.\d{2}\.\s*(?:[0-9]|1[0-9]|2[0-9]|3[0-1])\s*(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s*(?:19|20)\d{2}
But it did not work.
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I would appreciate it if someone can assist with a code to calculate the "specific number of heatwaves days where relative humidity > 66% and < 33%".
(whereas, a heatwave event is defined as one in which temperatures exceeded the 90th percentile of the daily mean temperature for at least three consecutive days, respectively).
Ok well here is a solution
# temperature percentile
cdo timpctl,90 infile -timmin infile -timmax t2m.nc t2m_pcen90.nc
# mask the temperature
cdo ge t2m.nc t2m_pcen90.nc mask.nc
# Need to make sure we have three consecutive days
cdo --timestat_date last runmean,3 mask.nc mask3.nc
cdo gec,1 mask3.nc heatwave_T.nc
# Now filter for dry heatwaves, assuming RH is %, change X if fraction
cdo lec,33 rh.nc rhdry.nc
cdo mul heatwave_T.nc rhdry.nc heatwave_dry.nc
# and same for wet
cdo gec,66 rh.nc rhwet.nc
cdo mul heatwave_T.nc rhwet.nc heatwave_wet.nc
Each file should have a 1 in it for each location/time when you are in a heatwave according to your definition. Of course the metadata is appropriate for T2m not the index, use NCO to change that if required. I have several video guides that would help with this question, the key one being the one on masking (it doesn't include the running mean part though). Note also that the RH criterion is applied ONLY on the day (no running mean) but that is how you write the definition in your question. Duplicate the running mean part if needed.
ps: In general it is good to show that you have attempted a solution yourself, before asking, SO guidelines are that questions are of a debugging nature, or can be a request for a one-liner, but not coding requests like "write me a code that does X or Y" - I think that is why you were getting downvoted.
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The answer to the question below is given as 2. Why does refresh delete only the first row? Is it not expected that it deletes all rows of an internal table?
What will be output by the following code?
DATA: BEGIN OF itab OCCURS 0, fval type i, END OF itab.
itab-fval = 1. APPEND itab.
itab-fval = 2. APPEND itab.
REFRESH itab.
WRITE: /1 itab-fval.
A: 1
B: 2
C: blank
D: 0
Answer: B
If the code did not contain any syntax errors, e.g. the missing '-' when assigning the value 2 and when writing the value, then B is the correct answer but not for the reason you state. It is not that the REFRESH only removes the first line from the table, it is because REFRESH does not clear the header line of the table. So after the REFRESH the header line still has the latest assigned value which is 2. This can be easily ascertained when running the program in the debugger.
Note that the use of internal table with header lines is obsolete, as mentioned in SAP help.
You can use a clear command to clear the header line.
REFRESH itab.
CLEAR itab.
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I've noticed twitter people search can come up with some weird results. Searching for match in screen_name twitter_name and bio is obvious, but they also do something different. I guess it has something to do with Triadic Closure but find its usage for search (instead of suggestions) weird. Wanted to hear your thoughts about this issue.
I think your question might be a little nonspecific, but here are my thoughts:
Suppose your search query was "Miley Cyrus", for instance. Now the top results will for sure include her real account, then fake ones, but then the results will get a little distorted.
I expect it ranks each account / person X in this manner (or something similar):
If person X follows accounts that has the search query in its bio / name, it has a higher rank than if that person didn't.
In our search, "Rock Mafia" is a good example; it doesn't have the term "Miley Cyrus" in its bio nor its name, but if you look at the people "Rock Mafia" is following, you'll find a lot of "similar" names / bios. Another ranking criteria would be this:
If person X has tweets that contains the search query in its content, it would also have a higher rank
A good example is the result "AnythingDisney" (#adljupdated), you can see that the 4th most recent tweet contains "Miley".
So basically the search prioritization looks like this:
Look in name / bio.
Need more results? Rank each person X by his followers and the people he follows, and by tweets that contain the query.
Need even more results? Look at "deeper" levels, rank each person X by the people being followed by the people X is following.
An so on, recursively.
I hope this helped in any manner!
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I have a set of training data consisting of 20 multiple choice questions (A/B/C/D) answered by a hundred respondents. The answers are purely categorical and cannot be scaled to numerical values. 50 of these respondents were selected for free product trial. The selection process is not known. What interesting knowledge can be mined from this information?
The following is a list of what I have come up with so far-
A study of percentages (Example - Percentage of people who answered B on Qs.5 and got selected for free product trial)
Conditional probabilities (Example - What is the probability that a person will get selected for free product trial given that he answered B on Qs.5)
Naive Bayesian classifier (This can be used to predict whether a person will be selected or not for a given set of values for any subset of questions).
Can you think of any other interesting analysis or data-mining activities that can be performed?
The usual suspects like correlation can be eliminated as the response is not quantifiable/scoreable.
Is my approach correct?
It is kind of reverse engineering.
For each respondent, you have 20 answers and one label, which indicates whether this respondent gets the product trial or not.
You want to know which of the 20 questions are critical to give trial or not decision. I'd suggest you first build a decision tree model on the training data. And study the tree carefully to get some insights, e.g. the low level decision nodes contain most discriminant questions.
The answers can be made numeric for analysis purposes, example:
RespondentID IsSelected Q1AnsA Q1AnsB Q1AnsC Q1AnsD Q2AnsA...
12345 1 0 0 1 0 0
Use association analysis to see if there are patterns in the answers.
Q3AnsC + Q8AnsB -> IsSelected
Use classification (such as logistic regression or a decision tree) to model how users are selected.
Use clustering. Are there distinct groups of respondents? In what ways are they different? Use the "elbow" or scree method to determine the number of clusters.
Do you have other info about the respondents, such as demographics? Pivot table would be good in that case.
Is there missing data? Are there patterns in the way that people skipped questions?