logistic-regression | A simple implementation of logisitic regression in Java | Testing library

 by   tpeng Java Version: Current License: No License

kandi X-RAY | logistic-regression Summary

kandi X-RAY | logistic-regression Summary

logistic-regression is a Java library typically used in Testing applications. logistic-regression has no bugs, it has no vulnerabilities and it has high support. However logistic-regression build file is not available. You can download it from GitHub.

A simple implementation of logisitic regression in Java.
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            kandi-support Support

              logistic-regression has a highly active ecosystem.
              It has 67 star(s) with 51 fork(s). There are 7 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 1 have been closed. On average issues are closed in 149 days. There are 1 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of logistic-regression is current.

            kandi-Quality Quality

              logistic-regression has 0 bugs and 9 code smells.

            kandi-Security Security

              logistic-regression has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              logistic-regression code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              logistic-regression does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              logistic-regression releases are not available. You will need to build from source code and install.
              logistic-regression has no build file. You will be need to create the build yourself to build the component from source.
              logistic-regression saves you 30 person hours of effort in developing the same functionality from scratch.
              It has 83 lines of code, 7 functions and 1 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed logistic-regression and discovered the below as its top functions. This is intended to give you an instant insight into logistic-regression implemented functionality, and help decide if they suit your requirements.
            • Classifies a dataset
            • Read a dataset from a file
            • Trains the model
            • Classify the sigmoid function
            • Compute sigmoid
            Get all kandi verified functions for this library.

            logistic-regression Key Features

            No Key Features are available at this moment for logistic-regression.

            logistic-regression Examples and Code Snippets

            Performs logistic regression .
            pythondot img1Lines of Code : 155dot img1no licencesLicense : No License
            copy iconCopy
            def main():
                Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
                print("Performing logistic regression...")
            
                N, D = Xtrain.shape
                Ytrain_ind = y2indicator(Ytrain)
                Ytest_ind = y2indicator(Ytest)
            
                # 1. full
                W = np.random.rand  
            Estimate the logistic regression .
            pythondot img2Lines of Code : 73dot img2no licencesLicense : No License
            copy iconCopy
            def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100):
                    # cast to float32
                    learning_rate = np.float32(learning_rate)
                    mu = np.float32(mu)
            
                    N, D = X.shape
                    K = len(set(Y))
            
                    self.hidden_la  
            Perform a logistic regression .
            pythondot img3Lines of Code : 54dot img3no licencesLicense : No License
            copy iconCopy
            def benchmark_full():
                Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
            
                print("Performing logistic regression...")
                # lr = LogisticRegression(solver='lbfgs')
            
            
                # convert Ytrain and Ytest to (N x K) matrices of indicator variables
               

            Community Discussions

            QUESTION

            how to plot the decision boundary of a polynomial logistic regression in python?
            Asked 2022-Apr-08 at 12:23

            I have looked into the example on this website: https://scipython.com/blog/plotting-the-decision-boundary-of-a-logistic-regression-model/

            I understand how they plot the decision boundary for a linear feature vector. But how would I plot the decision boundary if I apply

            ...

            ANSWER

            Answered 2022-Apr-08 at 10:39

            The output of your PolyCoefficients function is a 4th order polynomial made up of:

            Source https://stackoverflow.com/questions/71795205

            QUESTION

            How to efficiently run many logistic regressions in R and skip over equations that throw errors?
            Asked 2022-Apr-04 at 21:18

            As a continuation from this question, I want to run many logistic regression equations at once and then note if a group was significantly different from a reference group. This solution works, but it only works when I'm not missing values. Being that my data has 100 equations, it's bound to have missing values, so rather than this solution failing when it hits an error, how can I program it to skip the instances that throw an error?

            Here's a modified dataset that's missing cases:

            ...

            ANSWER

            Answered 2022-Apr-04 at 21:18

            One option would be purrr::safely which allows to take care of errors. To this end I use a helper function glm_safe which wraps your glm call inside purrr::safely. glm_safe will return a list with two elements, result and error. In case everything works fine result will contain the model object, while element is NULL. In case of an error the error message is stored in error and result will be NULL. To use the results in your pipeline we have to extract the result elements which could be achieved via transpose(reg)$result.

            Source https://stackoverflow.com/questions/71743544

            QUESTION

            Why does LogisticRegression give the same result every time, even with different random state?
            Asked 2022-Apr-01 at 19:34

            I am not an expert on logistic regression, but I thought when solving it using lgfgs it was doing optimization, finding local minima for the objective function. But every time I run it using scikit-learn, it is returning the same results, even when I feed it a different random state.

            Below is code that reproduces my issue.

            First set up the problem by generating data ...

            ANSWER

            Answered 2022-Apr-01 at 19:34

            First, let me put in the answer what got this closed as duplicate earlier: a logistic regression problem (without perfect separation) has a global optimum, and so there are no local optima to get stuck in with different random seeds. If the solver converges satisfactorily, it will do so on the global optimum. So the only time random_state can have any effect is when the solver fails to converge.

            Now, the documentation for LogisticRegression's parameter random_state states:

            Used when solver == ‘sag’, ‘saga’ or ‘liblinear’ to shuffle the data. [...]

            So for your code, with solver='lbfgs', indeed there is no expected effect.

            It's not too hard to make sag and saga fail to converge, and with different random_states to end at different solutions; to make it easier, set max_iter=1. liblinear apparently does not use the random_state unless solving the dual, so also setting dual=True admits different solutions. I found that thanks to this comment on a github issue (the rest of the issue may be worth reading for more background).

            Source https://stackoverflow.com/questions/71565977

            QUESTION

            How decision function of Logistic Regression on scikit-learn works?
            Asked 2022-Mar-07 at 00:41

            I am trying to understand how this function works and the mathematics behind it. Does decision_function() in scikitlearn give us log odds? The function return values ranging from minus infinity to infinity and it seems like 0 is the threshold for prediction when we are using decision_function() whereas the threshold is 0.5 when we are using predict_proba(). This is exactly the relationship between probability and log odds Geeksforgeeks.

            I couldn't see anything about that in the documentation but the function behaves like log-likelihood I think. am I right?

            ...

            ANSWER

            Answered 2022-Mar-06 at 22:14

            Decision function is nothing but the value of (as you can see in the source)

            Source https://stackoverflow.com/questions/71374316

            QUESTION

            Unable To Scrape url from page using Python and BeautifulSoup. Any ideas?
            Asked 2022-Feb-05 at 23:02

            As the title suggests. I'm playing around with a Twitter bot that scrapes rss feeds and tweets the title of the article and a link.

            For some reason when I run the below code it runs without errors but doesn't retrieve the url link. Any suggestions are gratefully recieved.

            ...

            ANSWER

            Answered 2022-Feb-05 at 22:40

            QUESTION

            Cost function for logistic regression: weird/oscillating cost history
            Asked 2022-Feb-02 at 05:07

            Background and my thought process:

            I wanted to see if I could utilize logistic regression to create a hypothesis function that could predict recessions in the US economy by looking at a date and its corresponding leading economic indicators. Leading economic indicators are known to be good predictors of the economy.

            To do this, I got data from OECD on the composite leading (economic) indicators from January, 1970 to July, 2021 in addition to finding when recessions occurred from 1970 to 2021. The formatted data that I use for training can be found further below.

            Knowing the relationship between a recession and the Date/LEI wouldn't be a simple linear relationship, I decided to make more parameters for each datapoint so I could fit a polynominal equation to the data. Thus, each datapoint has the following parameters: Date, LEI, LEI^2, LEI^3, LEI^4, and LEI^5.

            The Problem:

            When I attempt to train my hypothesis function, I get a very strange cost history that seems to indicate that I either did not implement my cost function correctly or that my gradient descent was implemented incorrectly. Below is the imagine of my cost history:

            I have tried implementing the suggestions from this post to fix my cost history, as originally I had the same NaN and Inf issues described in the post. While the suggestions helped me fix the NaN and Inf issues, I couldn't find anything to help me fix my cost function once it started oscillating. Some of the other fixes I've tried are adjusting the learning rate, double checking my cost and gradient descent, and introducing more parameters for datapoints (to see if a higher-degree polynominal equation would help).

            My Code The main file is predictor.m.

            ...

            ANSWER

            Answered 2022-Feb-02 at 05:07

            The problem you're running into here is your gradient descent function.

            In particular, while you correctly calculate the cost portion (aka, (hTheta - Y) or (sigmoid(X * Theta') - Y) ), you do not calculate the derivative of the cost correctly; in Theta = Theta - (sum((sigmoid(X * Theta') - Y) .* X)), the .*X is not correct.

            The derivative is equivalent to the cost of each datapoint (found in the vector hTheta - Y) multiplied by their corresponding parameter j, for every parameter. For more information, check out this article.

            Source https://stackoverflow.com/questions/70935019

            QUESTION

            logistic regression and GridSearchCV using python sklearn
            Asked 2021-Dec-10 at 14:14

            I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results

            After that I decided to try GridSearchCV. My questions below:

            1)

            ...

            ANSWER

            Answered 2021-Dec-09 at 23:12

            You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01

            Source https://stackoverflow.com/questions/70264157

            QUESTION

            Error: 'module' object is not callable in Doc2Vec
            Asked 2021-Oct-15 at 04:21

            I am trying to fit the Doc2Vec method in a dataframe which the first column has the texts, and the second one the label (author). I have found this article https://towardsdatascience.com/multi-class-text-classification-with-doc2vec-logistic-regression-9da9947b43f4, which is really helpful. However, I am stuck at how to build a model

            ...

            ANSWER

            Answered 2021-Aug-09 at 09:03

            i found this

            im not sure about Doc2Vec

            but this error in python is about module name

            This error statement TypeError: 'module' object is not callable is raised as you are being confused about the Class name and Module name. The problem is in the import line . You are importing a module, not a class. This happend because the module name and class name have the same name .

            If you have a class MyClass in a file called MyClass.py , then you should write:

            Source https://stackoverflow.com/questions/68709240

            QUESTION

            Why my Linear Regession model gives me error when all of my inputs are integers
            Asked 2021-Apr-15 at 14:38

            I want to try all regression algorithms on my dataset and choose a best. I decide to start from Linear Regression. But i get some error. I tried to do scaling but also get another error.

            Here is my code:

            ...

            ANSWER

            Answered 2021-Apr-15 at 14:38

            You're using LogisticRegression, which is a special case of Linear Regression used for categorical dependent variables.

            This is not necessarily wrong, as you might intend to do so, but that means you need sufficient data per category and enough iterations for the model to converge (which your error points out, it hasn't done).

            I suspect, however, that what you intended to use is LinearRegression (used for continuous dependent variables) from sklearn library.

            Source https://stackoverflow.com/questions/67110323

            QUESTION

            Hide scikit-learn ConvergenceWarning: "Increase the number of iterations (max_iter) or scale the data"
            Asked 2021-Apr-04 at 13:57

            I am using Python to predict values and getting many warnings like:

            Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( C:\Users\ASMGX\anaconda3\lib\site-packages\sklearn\linear_model_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

            this prevents me from seeing the my own printed results.

            Is there any way I can stop these warnings from showing?

            ...

            ANSWER

            Answered 2021-Apr-04 at 05:52

            You can use the warnings-module to temporarily suppress warnings. Either all warnings or specific warnings.

            In this case scikit-learn is raising a ConvergenceWarning so I suggest suppressing exactly that type of warning. That warning-class is located in sklearn.exceptions.ConvergenceWarning so import it beforehand and use the context-manager catch_warnings and the function simplefilter to ignore the warning, i.e. not print it to the screen:

            Source https://stackoverflow.com/questions/66938102

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

            Vulnerabilities

            No vulnerabilities reported

            Install logistic-regression

            You can download it from GitHub.
            You can use logistic-regression like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the logistic-regression component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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            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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