erfinv | The inverse error function

by lakshayg C++ Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | erfinv Summary

erfinv is a C++ library. erfinv has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitLab, GitHub.

Implementation of the inverse error function based on the rational approximation of percentage points of normal distribution available from erfinv(x) = --------- ppnd | ------- | sqrt(2) \ 2 /. The code has been tested on an x86_64 machine with Intel Core i7, the tests provided in this repository might not pass for different hardware configuration due to difference in floating point operations. golang's math library uses the same implementation for erfinv.

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erfinv has a low active ecosystem.

It has 6 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of erfinv is current.

Quality

erfinv has 0 bugs and 0 code smells.

Security

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

erfinv code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

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

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

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

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erfinv Key Features

No Key Features are available at this moment for erfinv.

erfinv Examples and Code Snippets

Mean squared error .

python

Lines of Code : 21

License : Non-SPDX (Apache License 2.0)

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def erfinv(x, name="erfinv"):
  """The inverse function for erf, the error function.

  Args:
    x: `Tensor` of type `float32`, `float64`.
    name: Python string. A name for the operation (default="erfinv").

  Returns:
    x: `Tensor` with `dtype=

Calculate the gradient of an op .

python

Lines of Code : 6

License : Non-SPDX (Apache License 2.0)

Copy

def _ErfinvGrad(op, grad):
  """Returns grad * sqrt(pi) / 2 * exp(erfinv(x)**2)."""
  root_pi_over_two = constant_op.constant(np.sqrt(np.pi) / 2, dtype=grad.dtype)
  with ops.control_dependencies([grad]):
    return grad * root_pi_over_two * math_ops

Community Discussions

Trending Discussions on erfinv

How to search a set of normally distributed parameters using optuna?

How to translate an Observable Notebook example into javascript?

Initialize Python Custom Distribution

Fitting a Custom Scipy Distribution

How to convert percentage to z-score of normal distribution in C/C++?

QUESTION

How to search a set of normally distributed parameters using optuna?

Asked 2021-Nov-12 at 01:19

I'm trying to optimize a custom model (no fancy ML whatsoever) that has 13 parameters, 12 of which I know to be normally distributed. I've gotten decent results using the hyperopt library:

...

ANSWER

Answered 2021-Nov-11 at 22:46

You can cheat optuna by using uniform distribution and transforming it into normal distribution. To do that one of the method is inversed error function implemented in scipy.

Function takes uniform distribution from in range <-1, 1> and converts it to standard normal distribution

Source https://stackoverflow.com/questions/69935219

QUESTION

How to translate an Observable Notebook example into javascript?

Asked 2021-Aug-13 at 01:14

I am trying to translate or convert this observable notebook example into plain javascript and d3 in order for me to use it in a website.

I tried to follow along this answer right here where something similar was achieved. But it didn't work for me, I get an error saying "Unexpected value NaN parsing cy attribute."

Here's what I have done until now:

...

ANSWER

Answered 2021-Aug-13 at 01:14

Here's a working example:

Source https://stackoverflow.com/questions/68741384

QUESTION

Initialize Python Custom Distribution

Asked 2021-Apr-12 at 14:26

I am using a custom log gamma distribution in Scipy and I want to use it with a variety of predefined Scipy Distributions. My issue is that it seems Custom Distributions need to be a handled a bit different than predefined distributions. Specifically, it seems I need to initialize the custom distribution before it can be used. See below:

...

ANSWER

Answered 2021-Apr-12 at 14:26

You are not initializing your distribution; it's just how SciPy's distribution infrastructure is built. You are confused between an instance of the rv_continuous class and the rv_frozen class.

When you call your distribution class (custom_lgamma in your case), SciPy generates documentation and code for the user interface which creates the distributions we see and use from scipy.stats (e.g. norm, gamma) (this is what happens when you do custom_lgamma()). This instance contains methods such as pdf, cdf, etc that are called with the shape parameters (just like other scipy.stats distributions).

Calling this instance again (e.g. norm()) will create a frozen distribution (an instance of the rv_frozen class) whose shape parameters cannot be changed (which is what would happen if you call your custom_lgamma instance again or do custom_lgamma()()). This instance has the pdf method that just accepts the quantile parameter.

Here is the convention that SciPy uses:

Source https://stackoverflow.com/questions/67059491

QUESTION

Fitting a Custom Scipy Distribution

Asked 2021-Feb-09 at 20:19

I have redefined the lognormal distribution using custom scipy class. I have simulated this distribution and I am trying to recover the original parameters I have specified, however, the fit method is returning different parameters.

...

ANSWER

Answered 2021-Feb-09 at 20:19

I've corrected code with proper _ppf, and it seems to produce proper fits for mu and sigma

Code, Python 3.9 Windows 10 x64

Source https://stackoverflow.com/questions/66122645

QUESTION

How to convert percentage to z-score of normal distribution in C/C++?

Asked 2020-Dec-16 at 00:27

The goal is to say: "These values lie within a band of 95 % of values around the mean in a normal distribution."

Now, I am trying to convert percentage to z-score, so then I can get the precise range of values. Something like would be enough.

So I need something like

...

ANSWER

Answered 2020-Dec-15 at 16:24

I say you were "close enough".

Source https://stackoverflow.com/questions/65309212

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

Vulnerabilities

No vulnerabilities reported

Install erfinv

You can download it from GitLab, GitHub.

Support

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