smf | A Lightweight Resource Provider for SMF in Chef

by livinginthepast Ruby Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | smf Summary

smf is a Ruby library. smf has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Service Management Facility (SMF) is a tool in many Illumos and Solaris-derived operating systems that treats services as first class objects of the system. It provides an XML syntax for declaring how the system can interact with and control a service. The SMF cookbook contains providers for creating or modifying a service within the SMF framework.

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Quality

Security

License

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Support

smf has a low active ecosystem.

It has 30 star(s) with 21 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 10 have been closed. On average issues are closed in 28 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of smf is current.

Quality

smf has 0 bugs and 0 code smells.

Security

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

smf code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

smf 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

smf 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.

smf saves you 239 person hours of effort in developing the same functionality from scratch.

It has 583 lines of code, 44 functions and 16 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Returns the XML for the XML document
Defines a list of resources
check if value is type
Authenticates credentials for this credential .
returns a Resource instance
Return the value of the current value .

Get all kandi verified functions for this library.

smf Key Features

No Key Features are available at this moment for smf.

smf Examples and Code Snippets

No Code Snippets are available at this moment for smf.

Community Discussions

Trending Discussions on smf

for loop to print logistic regression stats summary | statsmodels

Violation of UNIQUE KEY constraint in a composite key?

Error upon compilation while using jax.jit

Resulting array is zero when jax.numpy is used

Vectorization using numpy

Mixed Linear Models

LogisticRegression from sk_learn and smf.logit() from statsmodels.formula.api return different results

Difference between statsmodel OLS and scikit-learn linear regression

I have a question regarding having an inner function

rolling regression with a simple apply in pandas

QUESTION

for loop to print logistic regression stats summary | statsmodels

Asked 2022-Apr-16 at 08:11

I'm trying to figure out how to implement a for loop in statsmodels to get the statistics summary for a logistic regression (Iterate through independent variables list). I can get it to work fine with the traditional method, but using a for loop will make my life easier to find significance between variables.

Here is what I'm trying to do:

...

ANSWER

Answered 2022-Apr-16 at 08:11

def opportunites():
    indep = ['AGE', 'S0287', 'T0080', 'SALARY', 'T0329', 'T0333', 'T0159', 'T0165', 'EXPER', 'T0356']
    for i in indep:
        model = smf.logit(f'LEAVER ~ {i} ', data = df).fit()
        print(model.summary(
            yname="Status Leaver",
            xname=['Intercept', i],
            title=f'Logistic Regression of Leaver and {i}'
        ))
        print()

Source https://stackoverflow.com/questions/71891312

QUESTION

Violation of UNIQUE KEY constraint in a composite key?

Asked 2022-Mar-23 at 10:05

I am very new to SQL and am unable to understand this error: Violation of UNIQUE KEY constraint 'UQ__Flight_L__5DD08D7924EB8625'. Cannot insert duplicate key in object 'dbo.Flight_Leg'. The duplicate key value is (WN380)

...

ANSWER

Answered 2022-Mar-23 at 10:05

You have a uniqe constraint over Flight, which won't work

Source https://stackoverflow.com/questions/71585129

QUESTION

Error upon compilation while using jax.jit

Asked 2022-Feb-07 at 13:47

Pardon me I'm still a noob with the inner workings of Jax and trying to find my way around it. I have this code which works well without the jit. But when I try to jit it, it throws an error. I initially used an if else statement within the code which also did not work and had to rewrite the code this way without an if else statement. How do I get around this?. MWE is below.

...

ANSWER

Answered 2022-Feb-07 at 13:47

The issue is that indexing in JAX must be done with static values, and within JIT kvals[i] is not a static value (because it is computed from a JAX array).

One easy way to fix this in your case is to make kvals a non-jax array; for example when you define it, do this;

Source https://stackoverflow.com/questions/71016664

QUESTION

Resulting array is zero when jax.numpy is used

Asked 2022-Feb-07 at 13:36

I wrote the code below using numpy and got the correct output as shown in program 1. However when I switch to jax.numpy as jnp (in Program 2) the resulting output is an array of zeros. My MWE is shown below. I would like to know where I got the computation wrong? PS: the codes were run in different python files.

...

ANSWER

Answered 2022-Feb-07 at 09:50

Oh I already figured it out. I needed to make an explicit assignment.

Source https://stackoverflow.com/questions/71016283

QUESTION

Vectorization using numpy

Asked 2022-Feb-07 at 03:14

I translated this code from matlab.It is part of a larger body of code But I would like to get some advice on how to vectorize this section in order to make it faster. my major concern is with the for loops and if statements. If possible I would like to write it without using an if else statement. (Jax is not able to jit an if conditional). Thanks

...

ANSWER

Answered 2022-Feb-07 at 03:14

This is challenging to fully vectorize, but I assume it is possible. Here is a start that removes the if/else logic for the first loop. The trick is precomputing the k values (smf_1 is trivial so I removed it):

Source https://stackoverflow.com/questions/71012495

QUESTION

Mixed Linear Models

Asked 2022-Jan-24 at 07:39

I am trying to create a mixed linear model with the following data. I am trying to predict gambling from alcdep, with covariates age and sex. I am trying to use the statsmodels from python, but I am unsure as how to go about doing it.

So far I have tried: md = smf.mixedlm("acldep ~ Gambling", data, groups=data["Gambling"])

But I keep getting errors and I dont know how to specify the covariates using this way.

Here is the head of the data:

{'IID': {0: 'Yale_0001', 1: 'Yale_0004', 2: 'Yale_0006', 3: 'Yale_0007', 4: 'Yale_0008'}, 'SEX': {0: 2, 1: 1, 2: 2, 3: 1, 4: 1}, 'AGE': {0: 27, 1: 39, 2: 41, 3: 45, 4: 44}, 'alcdep': {0: 2, 1: 2, 2: 2, 3: 2, 4: 2}, 'Gambling': {0: 1, 1: 1, 2: 1, 3: 1, 4: 1}, 'Zero': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'Yes': {0: 'Yes', 1: 'Yes', 2: 'Yes', 3: 'Yes', 4: 'Yes'}, 'PRS': {0: 0.053486584299999994, 1: 0.0304387435, 2: 0.00917773968, 3: 0.016352741100000002, 4: 7.433452840000001e-05}}

...

ANSWER

Answered 2022-Jan-24 at 07:39

I have modified your data slightly because what you gave results in singular matrices.

You where almost there, by forgot some things. So, with this data:

Source https://stackoverflow.com/questions/70829665

QUESTION

LogisticRegression from sk_learn and smf.logit() from statsmodels.formula.api return different results

Asked 2021-Nov-28 at 14:38

I am trying to calculate the variance of the coefficients for logistic regression using bootstrap and I am using scikit-learn and statsmodels to compare results. I am using the Default dataset from the ISLR website which can be found in the zip forlder here or here as a plain csv file. I am using the following codes to perform the bootstrap:

Import the Dataset and create the response variable

...

ANSWER

Answered 2021-Nov-28 at 14:38

Although you set the C parameter to be high to minimize, sklearn by default uses lbfgs solver to find your optimal parameters while statsmodels uses newton .

You can try doing this to get similar coefficients:

Source https://stackoverflow.com/questions/70143564

QUESTION

Difference between statsmodel OLS and scikit-learn linear regression

Asked 2021-Nov-23 at 21:25

I tried to practice linear regression model with iris dataset.

...

ANSWER

Answered 2021-Nov-23 at 21:25

You included a full set of one-hot encoded dummies as regressors, which results in a linear combination that is equal to the constant, therefore you have perfect multicollinearity: your covariance matrix is singular and you can't take its inverse.

Under the hood both statsmodels and sklearn rely on Moore-Penrose pseudoinverse and can invert singular matrices just fine, the problem is that the coefficients obtained in the singular covariance matrix case don't mean anything in any physical sense. The implementations differ a bit between packages (sklearn relies on scipy.stats.lstsq, statsmodels has some custom procedure statsmodels.tools.pinv_extended, which is basically numpy.linalg.svd with minimal changes), so at the end of the day they both display «nonsense» (since no meaningful coefficients can be obtained), it's just a design choice of what kind of «nonsense» to display.

If you take the sum of coefficients of one-hot encoded dummies, you can see that for statsmodels it is equal to the constant, and for sklearn it is equal to 0, while the constant differs from statsmodels constant. The coefficients of variables that are not «responsible» for perfect multicollinearity are unaffected.

Source https://stackoverflow.com/questions/70081611

QUESTION

I have a question regarding having an inner function

Asked 2021-Nov-22 at 18:28

I have below code with an inner function, but it is not returning the "sorted_rsq" whenever I run it with arguments.

...

ANSWER

Answered 2021-Nov-22 at 18:28

Replace ols(formula,data) with return ols(formula,data) at the end of the outer function.

Source https://stackoverflow.com/questions/70070588

QUESTION

rolling regression with a simple apply in pandas

Asked 2021-Nov-14 at 21:25

Consider this simple example

...

ANSWER

Answered 2021-Nov-14 at 20:44

rolling apply is not capable of interacting with multiple columns simultaneously, nor is it able to produce non-numeric values. We instead need to take advantage of the iterable nature of rolling objects. We also need to account for handling min_periods ourselves, since the iterable rolling object generates all windows results regardless of other rolling arguments.

We can then create some function to produce each row in the results from the regression results to do something like:

Source https://stackoverflow.com/questions/69966707

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

Vulnerabilities

No vulnerabilities reported

Install smf

You can download it from GitHub.
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Support

file an issue to track updates/communication. rebase master into your branch. issue a pull request. Please do not increment the cookbook version in a fork. Version updates will be done on the master branch after any pull requests are merged. When upstream changes are added to the master branch while you are working on a contribution, please rebase master into your branch and force push. A pull request should be able to be merged through a fast-forward, without a merge commit.

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