I've been practising a little coding and I came across this problem. Need some tips on how to solve it. General programming question, just looking to see how you folks would solve it, preferred in python.
I need to get the amount of unordered combinations of 3s and 4s that add up to the input number.
Ex:
input is 12, combinations are 4+4+4 and 3+3+3+3, there are 2 combinations, output is 2
input is 13, combinations are 4+3+3+3, there is 1 combination, output is 1
input is 2, no combinations, output is 0
I'm aware there's a shortcut formula for this, but I'm wondering how you would do it without that.
Looks like a tree problem to me?
Related
i did a course on hamming and correcting code two years ago, and I had to go back on it recently for another course. I must admit I forget some part of it, I know the meaning of the terms and how to do the operations separately, but I can't figure how to visualize the linear code for these questions if someone could help me.
Questions
I have two audio files in which a sentence is read (like singing a song) by two different people. So they have different lengths. They are just vocal, no instrument in it.
A1: Audio File 1
A2: Audio File 2
Sample sentence : "Lorem ipsum dolor sit amet, ..."
I know the time every word starts and ends in A1. And I need to find automatically that what time every word starts and ends in A2. (Any language, preferably Python or C#)
Times are saved in XML. So, I can split A1 file by word. So, how to find sound of a word in another audio that has different duration (of word) and different voice?
So from what I read, it seems you would want to use Dynamic Time Warping (DTW). Of course, I'll leave the explanation for wikipedia, but it is generally used to recognize speech patterns without getting noise from different pronunciation.
Sadly, I am more well versed in C, Java and Python. So I will be suggesting python Libraries.
fastdtw
pydtw
mlpy
rpy2
With rpy2 you can actually use R's library and use their implementation of DTW in your python code. Sadly, I couldn't find any good tutorials for this but there are good examples if you choose to use R.
Please let me know if that doesn't help, Cheers!
My approach for this would be to record the dB volume at a constant interval (such as every 100 milliseconds) store this volume in a list or array. I found a way of doing this on java here: Decibel values at specific points in wav file. It is possible in other languages. Meanwhile, take note of the max volume:
max = 0;
currentVolume = f(x)
if currentVolume > max
{
max = currentVolume
}
Then divide the maximum volume by an editable threshold, in my example I went for 7. Say the maximum volume is 21, 21/7 = 3dB, let's call this measure X.
We second threshold, such as 1 and multiply it by X. Whenever the volume is greater than this new value (1*x), we consider that to be the start of a word. When it is less than the given value, we consider it to be the end of a word.
Visual explanation
Without knowing how sophisticated your understanding of the problem space is it isn't easy to know whether to point you in a direction or provide detail on why this problem is non-trivial.
I'd suggest that you start with something like https://cloud.google.com/speech/ and try to convert the speech blocks to text and then perform a similarity comparison on these.
If you really want to try to do the processing yourself you could look at doing some spectrographic analysis. Take the wave form data and perform an FFT to get frequency distributions and look for marker patterns that align your samples.
With only single word comparison of different speakers you are probably not going to be able to apply any kind of neural network unless you are able to train them on the 2 speakers entire speech set and use the network to then try to compare the individual word chunks.
It's been a few years since I did any of this so maybe it's easier these days but my recollection is that although this sounds conceptually simple it might prove to be more difficult than you realise.
The Dynamic Time Warping looks like the most promising suggestion.
secret sauce of below : pointA - pointB is zero if both points have same value ... that is numerically do a pointA minus pointB ... below leverages this to identify at what file byte index offset gives us this zero value when comparing the raw audio curves from a pair of input files ... or an close to zero in a relative sense if both source audio are different even slightly
approach is open up both files and pluck out the raw audio curve of each file ... define two variables bestSum and currentSum, set both to MAX_INT_VALUE ( any arbitrary high value ) ... iterate across the both files simultaneously and obtain the integer value of the current raw audio curve level of file A do same on other file B ... for each such integer just subtract the integer from file A from integer from file B ... continue this loop until you have reached end of one file ... inside of above loop add to currentSum variable the current value of the above mentioned subtraction ... at bottom of above loop update bestSum to become currentSum if currentSum < bestSum also store current file index offset ...
create an outer loop which does a repeat all of above by introducing an offset in time of one file then relaunch above inner loop ... your common audio is when you are using the offset which has the minimum total sum value .. that is the offset when you encountered bestSum
do not start coding until you have gained intuition that above makes perfect sense
I highly encourage you to plot out the curve of the raw audio for one file to confirm you are accessing this sequence of integers ... do this before attempting above algorithm
it will help to visualize above by viewing each input source audio as a curve and you simply keep one curve steady as you slide the other audio curve left or right until you see the curve shapes match or get very close to matching
I am trying to summarise some text using Gensim in python and want exactly 3 sentences in my summary. There doesn't seem to be an option to do this so I have done the following workaround:
with open ('speeches//'+speech, "r") as myfile:
speech=myfile.read()
sentences = speech.count('.')
x = gensim.summarization.summarize(speech, ratio=3.0/sentences)
However this code is only giving me two sentences. Furthermore, as I incrementally increase 3 to 5 still nothing happens.
Any help would be most appreciated.
You may not be able use 'ratio' for this. If you give ratio=0.3, and you have 10 sentences (assuming count of words in each sentence is same), your output will have 3 sentences, 6 for 20 and so on.
As per gensim doc
ratio (float, optional) – Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary.
Instead you might want to try using word_count, summarize(speech, word_count=60)
This question is a bit old, in case you found a better solution, pls share.
You have 12 shapes:
which you can make each out of five identical squares.
You need to combine the 12 pieces to one rectangle.
You can form four different rectangles:
2339 solutions (6x10), 2 solutions (3x20), 368 solutions (4x15), 1010 solutions (5x12).
I need to build the 3X20 rectangle:
My question what is the maximum number of states (i.e., the branching factor) that is possible?
My half way calculation:
The way I see it, there are 4 operations on each shape: turn 90/180/270 degrees and mirroring (turning it upside down).
Then, you have to put the shape on the board, somewhere on the 3X20 board.
Illegal states will be one that the shape doesn't fit in the board, but they are still states.
For the first move, you can chose each shape in 4 ways which is 4X12 ways, and then you need to multiply in the number of positions the shape can be in, and that is the number of states you have. But how can I calculate the number of positions?
Please help me with this calculation it is very important, it is not some kind of homework which I'm trying to avoid.
I think there is no easy & 'intelligent' way to list solutions (or states) to pentomino puzzles. You have to try all possibilities. Recursive programming or backtracking is the way to do it. You should check this solution that also has java source code available. Hopefully that points you to the right direction.
There is also a python solution that is perhaps more readable.
Could anyone please advise the best way to do the following?
I have three variables (X, Y & Z) and four groups (1, 2, 3 & 4). I have been using discriminant function analysis in SPSS to predict group membership of known grouped data for use with future ungrouped data.
Ideally I would like to able to randomly sample an increasing number of a subset of the data to see how many observations are required to hit a desired correct classification percentage.
However, I understand this might be difficult. Therefore, I'm looking to to do this for the means.
For example, Lets say variable X has a mean of 141 for group 1. This mean might have been calculated from 2000 observations. However, it might be the case that the mean occurred at say 700 observations. I would like to be able to calculate at what number of observations/cases the mean levels of in my data. For example, perhaps starting at 10 observations and repeating this randomly say 50 or 100 times, then increasing to 20 observations....and so on.
I understand this is a form of monte carlo testing. I have access to SPSS 15, 17 and 18 and excel. I also have access to minitab 15 & 16 and amos17 and have downloaded "R" but im not familiar with these. My experience is with SPSS and excel. I have tried some syntax in SPSS Modified from this..http://pages.infinit.net/rlevesqu/Syntax/RandomSampling/Select2CasesFromEachGroup.txt but this would still be quite time consuming on my part to enter the subset number ect etc.
Hope some one can help.
Thanks for reading.
Andy
The text you linked to is a good start (you can also use the SAMPLE command in SPSS, but IMO the Raynald script you linked to is more flexible when you think about constructing the sample that way).
In pseudo-code, the process might look like;
do n for sample size (a to b)
loop 100 times
draw sample size n
compute (& save) statistics
Here is where SPSS's macro language comes into play (I think this document is a good introduction, plus you can examine other references on the SPSS tag wiki). Basically once you figure out how to draw the sample and compute the stats you want, you just need to figure out how to write a macro so you can loop through the process (and pass it the sample size parameter). I include the loop 100 times because you want to be able to make some type of estimate about the error associated with each sample size.
If you give an example of how you compute the statistics I may be able to give examples of how to make that into a macro function and loop through the desired number of times.
#Andy W
#Oliver
Thanks for your suggestions guys. Ive managed to find a work around using the following macro from.........http://www.spsstools.net/Syntax/Bootstrap/GetRandomSampleOfVariousSizeCalcStats.txt However, for this I need to copy and paste the variable data for a given group into a new data window. Thats not to much of a problem. To take this further would anyone know how: 1/ I could get other statistics recorded eg std error, std dev ect ect. 2/Use other analysis, ideally discriminant function analysis and record in a new data window the percentage of correct classificcations rather than having lots of output tables 3/not need to copy and paste variables for each group so I can just run the macro specifying n samples for x variable on group 1, 2, 3 & 4.
Thanks again.
DEFINE !sample(myvar !TOKENS(1)
/nbsampl !TOKENS(1)
/size !CMDEND).
* myvar = the variable of interest (here we want the mean of salary)
* nbsampl = number of samples.
* size = the size of each samples.
!LET !first='1'
!DO !ss !IN (!size)
!DO !count = 1 !TO !nbsampl.
GET FILE='c:\Program Files\SPSS\employee data.sav'.
COMPUTE draw=uniform(1).
SORT CASES BY draw.
N OF CASES !ss.
COMPUTE samplenb=!count.
COMPUTE ss=!ss.
AGGREGATE
/OUTFILE=*
/BREAK=samplenb
/!myvar = MEAN(!myvar) /ss=FIRST(ss).
!IF (!first !NE '1') !THEN
ADD FILES /FILE=* /FILE='c:\temp\sample.sav'.
!IFEND
SAVE OUTFILE='c:\temp\sample.sav'.
!LET !first='0'
!DOEND.
!DOEND.
VARIABLE LABEL ss 'Sample size'.
EXAMINE
VARIABLES=salary BY ss /PLOT=BOXPLOT/STATISTICS=NONE/NOTOTAL
/MISSING=REPORT.
!ENDDEFINE.
* ----------------END OF MACRO ----------------------------------------------.
* Call macro (parameters are number of samples (here 20) and sizes of sample (here 5, 10,15,30,50).
* Thus 20 samples of size 5.
* Thus 20 samples of size 10, etc.
!sample myvar=salary nbsampl=20 size= 5 10 15 30 50.