I'm trying to produce a SKU in Google Sheets for a product using the values of three variants (Title, Colour and Size)
The product is 'Lightweight trainers' with colour variants of 'Red' and 'Blue', and the sizes range from 5 - 12.
Link to spreadsheet
https://docs.google.com/spreadsheets/d/1trq0X3MjR-n2THFnT8gYYlwKscnQavCeeZ8L-ifYaHw/edit?usp=sharing
Aim
I'm hoping to have a SKU that displays the product, the colour variant and the shoes size.
Example: LW-1-8 (Lightweight trainer, colour Red, size 8)
Product is Lightweight Trainers with a value of LW.
Colour variant 'Red' with a value of 1 and 'Blue' with a value of 2.
Shoe size variant = number ranging from 5 to 12.
Here's what I have so far, joining the colour and size variants.
=IFS(I2="Red",1,I2="Blue",2)&"-"& IFS(K2="5",5,K2="6",6,K2="7",7,K2="8",8,K2="9",9,K2="10",10,K2="11",11,K2="12",12)
However, I'm getting stuck in joining the data in column B with this function.
Any help with combining this data from multiple cells into one would be greatly appreciated.
TL;DR
=ARRAYFORMULA(IF(B2:B<>"", IFS(B2:B="Lightweight Trainers", "LW")&"-"&IFS(I2:I="Blue", 1, I2:I="Red", 2)&"-"&K2:K,))
Answer
What you want is basically:
<title>-<color number>-<shoe size>
To convert this to a function we can split it into each part and take it step by step:
Step 1: Title
For the first part -the title- we need to match the value with the shorthand. A simple list in an IFS is enough.
IFS(B2="Lightweight Trainers", "LW")
Obviously for now it only has a single value (Lightweight Trainers) but you could add more:
IFS(B2="Lightweight Trainers", "LW", B2="Heavyweight Trainers", "HW")
Step 2: color number
Similar to the previous step, it’s a mapping using ifs:
IFS(I2="Blue", "-1", I2="Red", "-2")
The dash is added so when adding everything it will only have it if
Step 3: shoe size
In this case we can simply get the value:
K2
Step 4: Adding everything together
We only need to add it with the dashes in between:
=IFS(B2="Lightweight Trainers", "LW")&"-"&IFS(I2="Blue", 1, I2="Red", 2)&"-"&K2
Step 5: Extending for the entire column automatically
We will use ARRAYFORMULA to add a single formula to the first cell and get it automatically extended to the entire column. We first add it to the formula we already have, and then extend the ranges to the entire column:
=ARRAYFORMULA(IFS(B2:B="Lightweight Trainers", "LW")&"-"&IFS(I2:I="Blue", 1, I2:I="Red", 2)&"-"&K2:K)
Remember to remove all the values in the column so array formula doesn’t override them (it would generate an error).
As you can see the formula generates errors for the rows that have no values. A good way of handling this case is to filter the rows without a title. In a single row would be:
=IF(B2<>"", [the entire formula],)
Notice the last comma.
So putting everything together and extending its range to the column, is:
=ARRAYFORMULA(IF(B2:B<>"", IFS(B2:B="Lightweight Trainers", "LW")&"-"&IFS(I2:I="Blue", 1, I2:I="Red", 2)&"-"&K2:K,))
Adding this to N2 should work.
Final notes
It seems that you use 150 when the size it’s not a whole number. If you want to keep that functionality you may use:
IF(K2-int(K2)=0, K2, 150)
On the last component and expand it the same way.
You may also want to prevent having two dashes when a value is missing (LW-5 instead of LW--5). To do so, I’d recommend adding it to each component instead of the formula that adds them together.
References
IFS (Docs Editors Help)
IF (Docs Editors Help)
ARRAYFORMULA (Docs Editors Help)
try in N2:
=IFS(I2="Red",1,I2="Blue",2)&"-"&
IFS(K2=5,5,K2=6,6,K2=7,7,K2=8,8,K2=9,9,K2=10,10,K2=11,11,K2=12,12)
or use:
=IF(I2="red", 1, IF(I2="blue", 2, )&IF((K5>=5)*(K5<=12), "-"&K5, )
What DataSetIterator should I use in order to create a DataSet object that contains miultiple features and labels? I have only seen examples similar to 'Iris example' where there is only one label and it is known how many different labels there are. In my problem there are four labels (position X, position Y, width and height of a shape) and many features (pixels values) and it's impossible to calculate how many different labels there could be.
I want something like this
RecordReader recordReader = new CSVRecordReader(0, ',');
recordReader.initialize(new FileSplit(new File(fileName)));
DataSetIterator iterator = new CustomDataSetIterator(recordReader, numRows, numFeatures, numLables);
DataSet allData = iterator.next();
Using data that looks like this
feature0;feature1;feature2;feature3;label0;label1;
I know that this question seems very basic and it is but I really had hard time finding any information about this topic in official tutorials or in documentation.
it seems like you are looking for an object detection kind of data with bounding boxes an multiple possible objects in your picture.
take a look at this example for that: https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/objectdetection/HouseNumberDetection.java
in general there is a MultiDataSet that can take multiple inputs and can have multiple outputs.
I want to compare two smoothing methods for a bigram model:
Add-one smoothing
Interpolated Absolute Discounting
For the first method, I found some codes.
def calculate_bigram_probabilty(self, previous_word, word):
bigram_word_probability_numerator = self.bigram_frequencies.get((previous_word, word), 0)
bigram_word_probability_denominator = self.unigram_frequencies.get(previous_word, 0)
if self.smoothing:
bigram_word_probability_numerator += 1
bigram_word_probability_denominator += self.unique__bigram_words
return 0.0 if bigram_word_probability_numerator == 0 or bigram_word_probability_denominator == 0 else float(
bigram_word_probability_numerator) / float(bigram_word_probability_denominator)
However, I found nothing for the second method except for some references for 'KneserNeyProbDist'. However, this is for trigrams!
How can I change my code above to calculate it? The parameters of this method must be estimated from a development-set.
In this answer I just clear up a few things that I just found about your problem, but I can't provide a coded solution.
with KneserNeyProbDist you seem to refer to a python implementation of that problem: https://kite.com/python/docs/nltk.probability.KneserNeyProbDist
There exists an article about Kneser–Ney smoothing on wikipedia: https://en.wikipedia.org/wiki/Kneser%E2%80%93Ney_smoothing
The article above links this tutorial: https://nlp.stanford.edu/~wcmac/papers/20050421-smoothing-tutorial.pdf but this has a small fault on the most important page 29, the clear text is this:
Modified Kneser-Ney
Chen and Goodman introduced modified Kneser-Ney:
Interpolation is used instead of backoff. Uses a separate discount for one- and two-counts instead of a single discount for all counts. Estimates discounts on held-out data instead of using a formula
based on training counts.
Experiments show all three modifications improve performance.
Modified Kneser-Ney consistently had best performance.
Regrettable the modified Version is not explained in that document.
The original documentation by Chen & Goodman luckily is available, the Modified Kneser–Ney smoothing is explained on page 370 of this document: http://u.cs.biu.ac.il/~yogo/courses/mt2013/papers/chen-goodman-99.pdf.
I copy the most important text and formula here as screenshot:
So the Modified Kneser–Ney smoothing now is known and seems being the best solution, just translating the description beside formula in running code is still one step to do.
It might be helpful that below the shown text (above in screenshot) in the original linked document is still some explanation that might help to understand the raw description.
I want to ask about linear mixed models.
The significance of the fixed variable is changed with a random structure.
For example, suppose there are 5 variables:
RT(response variable), covariate variable1(C.V.1), C.V.2, I.V.1, I.V.2.
all variables are within-subject variables excepting RT.
What I want to know is the interaction of I.V.2 and I.V.2.
In this situation, I set the two models using lmer().
First is that:
m1 <- lmer(rt ~ C.V.1 + C.V.2 + I.V.1*I.V.2 + (1+C.V.1 + C.V.2 + I.V.1*I.V.2|subject) + (1|word))
and second is that:
m2 <- lmer(rt ~ C.V.1 + C.V.2 + I.V.1*I.V.2 + (1+ I.V.1*I.V.2|subject) + (1|word))
when I analyzed this two models, the significance of fixed variable is different between the two models.
For example, the interaction of I.V.1 and I.V.2 is significant in m1, but not in m2.
I know setting the subject intercept means the responses would be different from each subject and setting the subject slope for I.V.1 means the effect of I.V.1 would be different from each subject.
But I don't know the relationship between fixed effects and random effects.
What is the meaning that considering random effects?
Can I interpret the result of the estimate of fixed variable as a coefficient when controlling the effect of other random effects like covariate variable?
And why the significance of fixed effect is changed with a random structure like the above two models?
Thank you for reading and I hope anyone would explain me these.
That i want to do is to delete certain rows (or columns doesn't really mater...) from a given vector.
By going through Simulink's components found out that there is nothing performing such an operation,there are blocks help one add elements but nothing clearly for removing,so ended up trying to delete them by using a function block and following the online examples that demonstrate the usage of "[]".Lets say that i want to delete the second column of the vector u,i do u(:, 2) = [];.
That works absolutely fine in a separate m file or function but unfortunately not in a function block returning:
"Simulink does not have enough information to determine output sizes for
this block. If you think the errors below are inaccurate, try specifying
types for the block inputs and/or sizes for the block outputs."
and:
Size mismatch (size [4 x 4] ~= size [4 x 3]).
The size to the left is the size of the left-hand side of the assignment.
Function 'MATLAB Function' (#107.41.42), line 4, column 1:
"u"
Launch diagnostic report.
Is there any alternative you can suggest to remove several elements in a given vector in Simulink?
Thanks in advance
George
Finally,managed to do it without function block.There is a much easier way,by using Pad,and defining the output vector to be shorter than the input resulting in truncation.