iminuit | Jupyter-friendly Python interface for C++ MINUIT2 | Machine Learning library
kandi X-RAY | iminuit Summary
kandi X-RAY | iminuit Summary
Jupyter-friendly Python interface for C++ MINUIT2
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Top functions reviewed by kandi - BETA
- Show a plot
- Return a MnMachinePrecision instance
- Estimate EDM goal
- Generate a Minuit
- Draw the minimization matrix
- Return the error probability for a given class cl
- Calculate the mn user profile
- Calculate the MnContour
- Convert to table
- Visualize the model
- Draw the mn profile
- Expand a callable
- Compute the Poisson density of a template
- Private method to set parameter value
- Compute the chi - squared chi squared
- Calculate the poisson distribution
- Wrapper function
- Set the loss function
- Evaluate the function
- Calculate the chi - squared chi coefficient for a template
- R Compute the Jacobian of a function
- Create a Minuit Minuit
- Draw a contour plot
- Return a table as a table
- Create a callable with the given replacement
- Visualize the components
iminuit Key Features
iminuit Examples and Code Snippets
cd .. # or cd anywhere outside your build folder
picca_deltas.py
def chi( paras ):
mpi = paras[ 0 : 32 ]
s = paras[ 32 ]
a = np.log( np.sum( np.array( mpi )**2 ) )
cf = a - np.exp( -s )
chif = 0
for i in range( 32 ):
chif += ( ( fpi - f( mpi, cf ) ) / error )**2
retur
void MnStrategy::SetMediumStrategy() {
// set minimum strategy (1) the default
fStrategy = 1;
SetGradientNCycles(3);
SetGradientStepTolerance(0.3);
SetGradientTolerance(0.05);
SetHessianNCycles(5);
SetHessianStepTolera
Community Discussions
Trending Discussions on iminuit
QUESTION
I am trying to fit a 5 parameter (a, b, c, d, e) model, where one of the parameters is constrained by another, let's say,
0< d < 1
e < |d|
I am currently using zfit which as far as I know, uses iMinuit
I have only created the zfit.Parameters
and put the limits such that the ranges accessible to them are valid, again, let's say:
d = zfit.Parameter('d', value=0.5, lower_limit=0.3, upper_limit=1.0, step_size=0.01)
e = zfit.Parameter('e', value=0.1, lower_limit=0.0, upper_limit=0.3, step_size=0.01)
It has been working well so far, but I think it is not the right way to do it.
So my question is, what is the correct way to deal with this kind of constraint?
Cheers
...ANSWER
Answered 2020-Jul-11 at 22:44I would use this limits with caution, as they block the variables, ideally, they should be far off the final value.
There are two ways to achieve what you want:
- either impose a constraint "mathematically" as a logical consequence, so define one parameter from another using a composed parameter (which is a function of other parameters). If possible, this should be the preferred way.
- Another option is to impose this restrictions in the likelihood with an additional term. This, however, can have repercussions as you modify the likelihood. The minimizer will find a minimum, but this is maybe not the minimum you have looked for. What you can use are SimpleConstraints and add a penalty term to the likelihood if any of the above is violated (e.g.
tf.cast(tf.greater(d, 1), tf.float64) * 100.
). Maybe make also sure that minuit is run with use_minuit_grad.
QUESTION
I am trying to add a constraint to a very complicated minimization problem I have but I am not sure how to implement it, even after reading the docs.
I have a simple example that if answered will help me with my original problem. Here is the code:
...ANSWER
Answered 2017-May-12 at 02:54Answer to my own question is don't bother using minuit. Use scipy.optimize with method SLSQP. It has equality and inequality constraint methods built in.
QUESTION
Does anyone knows what algorithm is used for the python iminuit fitting package, when no attribute is specified?
...ANSWER
Answered 2017-Jan-11 at 17:44The documentation says it uses the Quasi Newton Method and DFP formula.
You can see in the source code what the default strategy looks like:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install iminuit
You can use iminuit 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.
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