I am trying to use the VQC in Qiskit and create a custom feature map. To build the feature map I am using from qiskit.circuit the class ParameterVector, but this class does not let me use complex features.
Do you know any way I can still use the VQC while using the complex features?
Maybe this article can help you: 'Automatic design of quantum feature maps', Sergio Altares-López, Angela Ribeiro and Juan José García-Ripoll, 19 August 2021 - https://doi.org/10.1088/2058-9565/ac1ab1.
They describe a technique to generate optimal quantum feature maps by using multiobjetive genetic algorithms. While the first objective is to increase the accuracy in the predictions on unseen data, building robust classifiers with generalisation power; the second objective of this technique is to reduce the complexity of the quantum circuits.
In this study they use 22 features in a quantum model.
Related
Both Kaplan Meier method and Logistic Regression have their own feature selections. I want to use another method to pick best features for example, back stepwise feature selection. Is it possible to use this sort of methods instead or not.
My data acquires more than 130 features and about 3000 individuals. Since it is medical [cancer] data I don't want to use simple methods.
Further information about the project can be seen here and it is in order of what should I do:
preprocessing the data
separating them for test and train
Data imputation for train data
Feature selection by train data
Training the models which are Kaplan Meier and Logistic Regression
Testing the model
Pleas inform me that is it wrong to use any other feature selection for them or not?
I can use any tip about the model which I have listed too.
Basically there are 4 types of feature selection (fs) techniques namely:-
1.) Filter based fs
2.) Wrapper based fs
3.) Embedded fs techniques
4.) Hybrid fs techniques
Each has it's own advantages and disadvantages. For ex, filter fs is used when you want to determine if "one" feature is important to the output variable. So if you have 400 features in your dataset, you would have to repeat this 400 times!
Wrapper based methods (as you mentioned in you question), on the other hand do this is one step. But they are prone to overfitting, whereas filter based methods are not.
Embedded methods use tree based methods for fs purpose.
I do not have enough knowledge about hybrid methods.
I would say you could use some wrapper based techniques like RFECV since you say you do not want to use simple filter techniques.
Anyone have good book / article recommendation for procedural generation of background music? (No vocals, just instruments).
I'm not interested in:
How do I generate the sound of a particular note on a particular instrument
I'm interested in:
How do I generate the melody / score for the music.
Thanks!
EDIT:
Thanks for the reference to Brian Eno. I'm definitely looking into the ambient/user can ignore type of music. I.e. think the background music of a game. It's there to provide some basic mood, but the focus is the game.
Sometime ago I ran into ChucK, which is a programming-language to generate music/sound/audio:
ChucK presents a new time-based, concurrent programming model that's highly precise and expressive (we call this strongly-timed), as well as dynamic control rates, and the ability to add and modify code on-the-fly. In addition, ChucK supports MIDI, OSC, HID device, and multi-channel audio. It's fun and easy to learn, and offers composers, researchers, and performers a powerful programming tool for building and experimenting with complex audio synthesis/analysis programs, and real-time interactive control.
I believe the end result can be converted into MIDI, which can then be converted into a score or sheet notation.
I don't know if this is what you're looking for. Hope this helps!
EDIT
After thinking about this a little longer, I think what you can possibly do (and this sounds a bit crazy) is write code that generates ChucK code. So define a set of rules for your music/score generation and then use that to create valid ChucK code. After you run the ChucK code, you can get a MIDI file which you can then convert into score/sheet-music.
The book "Computer Models of Musical Creativity" by David Cope should help you along with the theoretical side of computer-assisted composition, though you might want some music theory under your belt before you dive in.
If you are interested in procedural music check out the Condition30 site -- condition30.com
This music is all procedural.
If you're interested in an implementation of procedural music based on cellular automata in C#, you could grab the source code from http://proceduralmidi.codeplex.com/. A binary is also available.
For a linguistics course we implemented Part of Speech (POS) tagging using a hidden markov model, where the hidden variables were the parts of speech. We trained the system on some tagged data, and then tested it and compared our results with the gold data.
Would it have been possible to train the HMM without the tagged training set?
In theory you can do that. In that case you would use the Baum-Welch-Algorithm. It is described very well in Rabiner's HMM Tutorial.
However, having applied HMMs to part of speech, the error you get with the standard form will not be so satisfying. It is a form of expectation maximization which only converges to local maxima. Rule based approaches beat HMMs hands down, iirc.
I believe the natural language toolkit NLTK for python has an HMM implementation for that exact purpose.
NLP was a couple years ago, but I believe without tagging the HMM could help determine the symbol emission/state transition probabilities of n-grams (i.e. what are the odds of "world" occurring after "hello"), but not parts-of-speech. It needs the tagged corpus to learn how the POS interrelate.
If I'm way off on this let me know in the comments!
I'm trying to implement a fuzzy logic membership function in C for a hobby robotics project but I'm not quite sure how to start.
I have inputs about objects near a point, such as distance or which directions are clear/obstructed, and I want to map how strongly these inputs belong to sets like very near, near, far, very far. Does anyone have a tip on how to start? Thanks.
Disclaimer: I've never implemented a fuzzy controller (I've only ever used PI or PID in real-life) and control class was 10 years ago.
Here's an presentation demonstrating moving towards a target using distance and angle for inputs and power as the output. FuzzyTech's Example positioning a crane
This just presents the topic and theory i.e. no code.
Best source is probably one of the robotics groups
e.g Seattle Robotic Society fuzzy logic tutorial it is technical ... and long.
if you can access technical journals then search Google scholar for "fuzzy logic" "path planning" robotics
if you're looking for some ideas on how to implement fuzzy logic then perhaps a Application Note from one of the microchip manufactures will get you started e.g Microchip's paper on Airflow control or servo control. I know it's not Arduino but Microchips papers are usually very clearly presented.
And finally an example in c++ its probably more complex than you're looking for. Free fuzzy logic library
Good luck.
I'm not expert with fuzzy logic, but according to my basic understanding, you could start by deciding what distances would constitute near (say 10 cm) far (say 1m), then you use probabilities to fill in the range in between (so 55cm might be 50% near, 50% far). Then you do something similar for your other properties, and combine the probabilities associated with each property with more probabilities.
Do you have a good reference for designing fuzzy controls?
I suppose you could start here. I think they at least describe simple fuzzification and defuzzification routines.
The guys at MakeProto have created an automatic code generator for Fuzzy Systems that outputs C code from Matlab fuzzy systems, or by a hand-defined fuzzy system.
Might be worth taking a look at.
http://makeproto.com/blog/?p=35
Fuzzy inference system can be implemented in both C and C++. Learn How to frame fuzzy logic in c
What business cases are there for using Markov chains? I've seen the sort of play area of a markov chain applied to someone's blog to write a fake post. I'd like some practical examples though? E.g. useful in business or prediction of stock market, or the like...
Edit: Thanks to all who gave examples, I upvoted each one as they were all useful.
Edit2: I selected the answer with the most detail as the accepted answer. All answers I upvoted.
The obvious one: Google's PageRank.
Hidden Markov models are based on a Markov chain and extensively used in speech recognition and especially bioinformatics.
I've seen spam email that was clearly generated using a Markov chain -- certainly that qualifies as a "business use". :)
There is a class of optimization methods based on Markov Chain Monte Carlo (MCMC) methods. These have been applied to a wide variety of practical problems, for example signal & image processing applications to data segmentation and classification. Speech & image recognition, time series analysis, lots of similar examples come out of computer vision and pattern recognition.
We use log-file chain-analysis to derive and promote secondary and tertiary links to otherwise-unrelated documents in our help-system (a collection of 10m docs).
This is especially helpful in bridging otherwise separate taxonomies. e.g. SQL docs vs. IIS docs.
I know AccessData uses them in their forensic password-cracking tools. It lets you explore the more likely password phrases first, resulting in faster password recovery (on average).
Markov chains are used by search companies like bing to infer the relevance of documents from the sequence of clicks made by users on the results page. The underlying user behaviour in a typical query session is modeled as a markov chain , with particular behaviours as state transitions...
for example if the document is relevant, a user may still examine more documents (but with a smaller probability) or else he may examine more documents (with a much larger probability).
There are some commercial Ray Tracing systems that implement Metropolis Light Transport (invented by Eric Veach, basically he applied metropolis hastings to ray tracing), and also Bi-Directional- and Importance-Sampling- Path Tracers use Markov-Chains.
The bold texts are googlable, I omitted further explanation for the sake of this thread.
We plan to use it for predictive text entry on a handheld device for data entry in an industrial environment. In a situation with a reasonable vocabulary size, transitions to the next word can be suggested based on frequency. Our initial testing suggests that this will work well for our needs.
IBM has CELM. Check out this link:
http://www.research.ibm.com/journal/rd/513/labbi.pdf
I recently stumbled on a blog example of using markov chains for creating test data...
http://github.com/emelski/code.melski.net/blob/master/markov/main.cpp
Markov model is a way of describing a process that goes through a series of states.
HMMs can be applied in many fields where the goal is to recover a data sequence that is not immediately observable (but depends on some other data on that sequence).
Common applications include:
Crypt-analysis, Speech recognition, Part-of-speech tagging, Machine translation, Stock Prediction, Gene prediction, Alignment of bio-sequences, Gesture Recognition, Activity recognition, Detecting browsing pattern of a user on a website.
Markov Chains can be used to simulate user interaction, f.g. when browsing service.
My friend was writing as diplom work plagiat recognision using Markov Chains (he said the input data must be whole books to succeed).
It may not be very 'business' but Markov Chains can be used to generate fictitious geographical and person names, especially in RPG games.
Markov Chains are used in life insurance, particularly in the permanent disability model. There are 3 states
0 - The life is healthy
1 - The life becomes disabled
2 - The life dies
In a permanent disability model the insurer may pay some sort of benefit if the insured becomes disabled and/or the life insurance benefit when the insured dies. The insurance company would then likely run a monte carlo simulation based on this Markov Chain to determine the likely cost of providing such an insurance.