aDDM-Toolbox | data analysis using the attentional drift | Predictive Analytics library
kandi X-RAY | aDDM-Toolbox Summary
kandi X-RAY | aDDM-Toolbox Summary
This toolbox can be used to perform model fitting and to generate simulations for the attentional drift-diffusion model (aDDM), as well as for the classic version of the drift-diffusion model (DDM) without an attentional component.
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Top functions reviewed by kandi - BETA
- Compute the likelihood of an individual
- Compute the likelihood of a trial
- Plot a trial
- Compute the log - likelihood
- Simulate a trial
- Compute the log - likelihood of the model
- Simulate a CDM trial
- Calculate the model log likelihood
- Wrap AddM
- Wraps DDM get_trial_likelihood
aDDM-Toolbox Key Features
aDDM-Toolbox Examples and Code Snippets
Lines of Code : 2License : Strong Copyleft (GPL-3.0)
Copy$ addm_demo --display-figures
$ addm_demo --help
$ pip install addm_toolbox
$ addm_toolbox_tests
Community Discussions
Trending Discussions on Predictive Analytics
QUESTION
GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
...ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
...ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
QUESTION
I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
...ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install aDDM-Toolbox
addm_demo
ddm_pta_test
addm_pta_test
addm_pta_mle
addm_pta_map
addm_simulate_true_distributions
addm_basinhopping
addm_genetic_algorithm
ddm_mla
addm_mla
addm.py contains the aDDM implementation, with functions to generate model simulations and obtain the likelihood for a given data trial.
ddm.py is equivalent to addm.py but for the DDM.
addm_pta_test.py generates an artificial data set for a given set of aDDM parameters and attempts to recover these parameters through maximum a posteriori estimation.
ddm_pta_test.py is equivalent to addm_pta_test.py but for the DDM.
addm_pta_mle.py fits the aDDM to a data set by performing maximum likelihood estimation.
addm_pta_map.py performs model comparison for the aDDM by obtaining a posterior distribution over a set of models.
simulate_addm_true_distributions.py generates aDDM simulations using empirical data for the fixations.
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