nelder_mead | A Python easy implementation of the Nelder-Mead method | Code Coverage Tools library

by owruby Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | nelder_mead Summary

nelder_mead is a Python library typically used in Code Quality, Code Coverage Tools, Bilibili applications. nelder_mead has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

The Nelder-Mead is the one of derivative-free optimization method. This method is called simplex method or ameba method.

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Support

nelder_mead has a low active ecosystem.

It has 13 star(s) with 8 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

nelder_mead has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of nelder_mead is current.

Quality

nelder_mead has no bugs reported.

Security

nelder_mead has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

nelder_mead is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

nelder_mead releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed nelder_mead and discovered the below as its top functions. This is intended to give you an instant insight into nelder_mead implemented functionality, and help decide if they suit your requirements.

Minimize the problem
Calculate optimal solution
Evaluate the function
Return the centroid of the grid
Generate a point
Expand a point to a point
Return True if point is inside self
Return the point outside of the ellipse
Reflect a point
Optimizes the model

Get all kandi verified functions for this library.

nelder_mead Key Features

No Key Features are available at this moment for nelder_mead.

nelder_mead Examples and Code Snippets

No Code Snippets are available at this moment for nelder_mead.

Community Discussions

Trending Discussions on nelder_mead

Likelihood Ratio Test for LMM gives P-Value of 1?

Optimization in Julia with Optim.jl - How do I get rid of this error?

Different gradient calculations in a logistic regression

Scaling predictors in lme4 glmer doesn't resolve eigenvalue warnings; neither does alternative optimization

Miximum Likelihood - using Optim package

lme warning message because of random effects

QUESTION

Likelihood Ratio Test for LMM gives P-Value of 1?

Asked 2021-Feb-26 at 18:29

I did an experiment in which people had to give answers to moral dilemmas that were either personal or impersonal. I now want to see if there is an interaction between the type of dilemma and the answer participants gave (yes or no) that influences their reaction time. For this, I computed a Linear Mixed Model using the lmer()-function of the lme4-package. My Data looks like this:

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ANSWER

Answered 2021-Feb-26 at 18:25

You say

logRT is the average logarithmized reaction time across all those 4 dilemmas.

If I'm interpreting this correctly — i.e., each subject has the same response for all of the times they are observed — then this is the proximal cause of your problem. (I know I've seen this exact problem before, but I don't know where — here? r-sig-mixed-models@r-project.org?)

simulate data

Source https://stackoverflow.com/questions/66352170

QUESTION

Optimization in Julia with Optim.jl - How do I get rid of this error?

Asked 2021-Jan-06 at 13:59

newbie here

I'm trying to minimize a function in Julia with optim.jl. The function works, but when I try to optimize it it gives me this error message:

...

ANSWER

Answered 2021-Jan-06 at 12:19

You can replicate your error via:

Source https://stackoverflow.com/questions/65594515

QUESTION

Different gradient calculations in a logistic regression

Asked 2020-Jun-26 at 12:55

I am trying to find how one variable (EVI) may predict a binary outcome (an_larv_bin) using glmer from lme4 package. The code I input is:

...

ANSWER

Answered 2020-Jun-26 at 12:55

data:

Source https://stackoverflow.com/questions/62593506

QUESTION

Scaling predictors in lme4 glmer doesn't resolve eigenvalue warnings; neither does alternative optimization

Asked 2018-Dec-21 at 20:12

I am analysing data (included below) using lme4's glmer function in R. The model I am building consists of a Poisson-distributed response variable (obs), one random factor (area), one continuous offset (duration), five continuous fixed effects (can_perc, can_n, time, temp, cloud_cover) and one binomial fixed effect factor (burnt). Before fitting the model I checked for collinearity and removed any collinear variables.

The initial model is:

...

ANSWER

Answered 2018-Dec-21 at 20:12

tl;dr This looks like a case of complete separation; you have no positive outcomes at all in your "burned" condition. You don't necessarily need to worry about this - the AIC comparisons should still be reasonably robust - but you might want to understand what's going on before you proceed. This problem (and remedies) are discussed in a relevant section of the GLMM FAQ (and there are a variety of relevant questions/answers on CrossValidated).

How do I know? Here are the coefficients:

Source https://stackoverflow.com/questions/53834754

QUESTION

Miximum Likelihood - using Optim package

Asked 2018-Jun-01 at 06:40

Dear users of the language julia. I have a problem when using the optimize function of the Optim package. What is the error of the code below?

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ANSWER

Answered 2018-Jun-01 at 06:40

The reason of your problem is that your definition of pdf_weibull is incorrect. Here is a corrected definition:

Source https://stackoverflow.com/questions/50633483

QUESTION

lme warning message because of random effects

Asked 2017-Oct-25 at 16:03

I have a data frame with 5 variables: Lot / Wafer / Serial Number / Voltage / Amplification. In this data frame there are 1020 subsets grouped by Serial_number. Each subset has a certain number of measurement data points (Amplification against voltage).

I fit the data with

...

ANSWER

Answered 2017-Oct-25 at 16:03

The convergence warnings disappeared when I removed all data points <2. I stumbled over this by coincidence..

Probably this is somehow connected to the issue that for each subset within the range from 0 to about 50 all data points are almost exactly the same (and have values of about ~1).

Source https://stackoverflow.com/questions/46728349

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install nelder_mead

You can download it from GitHub.
You can use nelder_mead like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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