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I came across some answers regarding the possibility of image recognition (and further matching) in Python and other languages, as well as papers describing a machine learning process for matching signatures*, but none in R regarding the possibility of recognizing signatures. In Python, a language in which I might try a hand at, I found the OpenCV library for face recognition. But still is not exactly what I need.
For an R user (and someone willing to instruct himself in Python), what is the best strategy -- considering time, nothing that it would take more than a few days of learning -- to create an algorithm for signature matching in R or Python?
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I have been working on an algorithm trading project where I used R to fit a random forest using historical data while the real-time trading system is in Python.
I have fitted a model I'd like to use in R and am now wondering how can I use this model for prediction purposes in the Python system.
Thanks.
There are several options:
(1) Random Forest is a well researched algorithm and is available in Python through sci-kit learn. Consider implementing it natively in Python if that is the end goal.
(2) If that is not an option, you can call R from within Python using the Rpy2 library. There is plenty of online help available for this library, so just do a google search for it.
Hope this helps.
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I need to identify the songs by human hum. what are the best methodology and algorithm that i can use for achieve that. I search for code samples. But I couldn't find. Please help me....
You could begin a python program that uses tensorflow to deep-learn the correspondence between humming and songs - it should fall under the umbrella initiative by Google Brain called Magenta.
Of course for Deep-Learning you would need to have a large corpus of examples to learn from.
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What I am trying to do is to create a Multiple Choice Question (MCQ) generation to our fill in the gap style question generator. I need to generate distracters (Wrong answers) from the Key (correct answer). The MCQ is generated from educational texts that users input. We're trying to tackle this through combining Contextual similarity, similarity of the sentences in which the keys and the distractors occur in and Difference in term frequencies Any help? I was thinking of using big data datasets to generate related distractors such as the ones provided by google vision, I have no clue how to achieve this in python.
This question is way too broad to be answered, though I would do my best to give you some pointers.
If you have a closed set of potential distractors, I would use word/phrase embedding to find the closest distractor to the right answer.
Gensim's word2vec is a good starting point in python
If you want your distractors to follow a template, for example replace a certain word from the right answer with its opposite, I would use nltk's wordnet implementation to find antonyns / synonyms.
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Data visualization (python preferred but not essential):
I have some data which fits into daily chunks nicely for a period of a few months, how may I produce an infographic/calender-type graph analogous to this (found online):
My preferred medium is Python (would htmlCalender suffice?) but anything to accomplish this will be fine.
I would suggest that you take a look at D3.js.
If you look at the examples there is a Calendar View that does something closely to what you want to accomplish.
Another example of a similar calendar visualization is Cal-heatmap.
You will find more libraries with similar concepts if you google for "calendar heatmap".
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I'm wondering if I can extract a sequence of musical notes from a recorded sound using Python.
It is the first time I'm considering using Python for this.
Help would be truly awesome :)
What you would want to do is take your audio samples, convert them into the frequency domain with a Fast Fourier Transform (FFT), find the most powerful frequency in the sample, and convert that frequency into a note.
See FFT for Spectrograms in Python for pointers to libraries to help with the first two items. See http://80.68.92.234/sigproc.html for some sample code to get you started.