iris | Decentralized cloud | Machine Learning library

 by   project-iris Go Version: v0.3.2 License: Non-SPDX

kandi X-RAY | iris Summary

kandi X-RAY | iris Summary

iris is a Go library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Wordpress applications. iris has no bugs, it has no vulnerabilities and it has low support. However iris has a Non-SPDX License. You can download it from GitHub.

iris is an attempt at bringing the simplicity and elegance of cloud computing to the application layer. consumer clouds provide unlimited virtual machines at the click of a button, but leaves it to developer to wire them together. iris ensures that you can forget about networking challenges and instead focus on solving your own domain problems. it is a completely decentralized messaging solution for simplifying the design and implementation of cloud services. among others, iris features zero-configuration (i.e. start it up and it will do its magic), semantic addressing (i.e. application use textual names to address each other), clusters as units (i.e. automatic load balancing between apps of the same name) and perfect secrecy (i.e. all network traffic is encrypted). you can find further infos on the [iris website] and details of the above features in the [core concepts] section of [the book
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              iris has a low active ecosystem.
              It has 578 star(s) with 32 fork(s). There are 48 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 34 open issues and 24 have been closed. On average issues are closed in 15 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of iris is v0.3.2

            kandi-Quality Quality

              iris has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              iris has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              iris releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

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            iris Key Features

            No Key Features are available at this moment for iris.

            iris Examples and Code Snippets

            Generates the ConfusionMatrix of IRIS dataset .
            pythondot img1Lines of Code : 32dot img1License : Permissive (MIT License)
            copy iconCopy
            def main():
            
                """
                Random Forest Classifier Example using sklearn function.
                Iris type dataset is used to demonstrate algorithm.
                """
            
                # Load Iris dataset
                iris = load_iris()
            
                # Split dataset into train and test data
                X = ir  

            Community Discussions

            QUESTION

            Custom function to check the data type of each column
            Asked 2021-Jun-15 at 13:10

            I am creating a function that runs through my variables and determines if they are numeric. If the variable is numeric, I want it to print the mean, median, variance, mode and range. And if it is not numeric, I want it to print just the mode. However it doesn't work not sure if I am using the right function (typeof & class)

            I receive below error

            ...

            ANSWER

            Answered 2021-Jun-15 at 13:10

            Don't use $ inside functions, we can use [[ to extract a particular columns.

            You can modify the function as follows -

            Source https://stackoverflow.com/questions/67986568

            QUESTION

            From the “iris” dataset, how to find the number of observations whose “Sepal.Length” is greater than ‘6.5’
            Asked 2021-Jun-15 at 03:09

            From the “iris” dataset, how to find the number of observations whose “Sepal.Length” is greater than ‘6.5’ Using only loops or conditional statements

            ...

            ANSWER

            Answered 2021-Jun-15 at 02:27
            dat <- iris[iris$Sepal.Length > 6.5, ]
            nrow(dat)
            

            Source https://stackoverflow.com/questions/67979226

            QUESTION

            How to use if_else and mutate to substitute the value in charactor vector as condition?
            Asked 2021-Jun-14 at 16:54

            I want to generate one column in data with previous value if the condition in if_else are/aren`t consistent with, the value will be the same as the original column.

            Here is the code:

            ...

            ANSWER

            Answered 2021-Jun-14 at 07:45

            You can use the following -

            Source https://stackoverflow.com/questions/67966432

            QUESTION

            Creating new columns with mutate() and across()
            Asked 2021-Jun-14 at 16:11

            This is a simplified version of the actual problem I'm dealing with. In this example, I'll be working with four columns, and the actual problem requires working with about 20-30 columns.

            Consider the iris dataset. Suppose that I wanted to, for some reason, append new columns which would be equal to double the .Length and the .Width columns. With the following code, this would change the existing columns:

            ...

            ANSWER

            Answered 2021-Jun-14 at 16:10

            We can use across (used dplyr 1.0.6 version)

            Source https://stackoverflow.com/questions/67973706

            QUESTION

            Share one y axis for four different boxplots
            Asked 2021-Jun-14 at 15:15

            I work with the iris dataset, the aim is to get 4 boxplots next to each other and make them all share an y-axis that goes from 0 to 8

            ...

            ANSWER

            Answered 2021-Jun-14 at 15:15

            Three options:

            base graphics

            Determine the y range before plotting. For this there are two options, choose from one of the ylim= below:

            Source https://stackoverflow.com/questions/67972090

            QUESTION

            SHAP DeepExplainer with TensorFlow 2.4+ error
            Asked 2021-Jun-14 at 14:52

            I'm trying to compute shap values using DeepExplainer, but I get the following error:

            keras is no longer supported, please use tf.keras instead

            Even though i'm using tf.keras?

            ...

            ANSWER

            Answered 2021-Jun-14 at 14:52

            TL;DR

            • Add tf.compat.v1.disable_v2_behavior() at the top for TF 2.4+
            • calculate shap values on numpy array, not on df

            Full reproducible example:

            Source https://stackoverflow.com/questions/66814523

            QUESTION

            How to adjust geom_point dodge width to match geom_boxplot width when varwidth = TRUE?
            Asked 2021-Jun-14 at 04:59

            Is there a way to match ggplot geom_point position dodging width to a geom_boxplot width that is adjusted to the number of data points using the varwidth = TRUE option in geom_boxplot? This would require different dodging widths for each group. Demonstration:

            ...

            ANSWER

            Answered 2021-Apr-27 at 16:28

            It is because you only specify 3 values, but you have many more points. One way to do this is to specify every point:

            Source https://stackoverflow.com/questions/67286817

            QUESTION

            how can I improve this confusion matrix in R?
            Asked 2021-Jun-12 at 09:20

            Using the iris dataset in R, I write a function to plot a confusion matrix.

            ...

            ANSWER

            Answered 2021-Jun-12 at 09:19

            You can create separate column for labels. For 0 frequency make them as blank.

            Source https://stackoverflow.com/questions/67946452

            QUESTION

            Theta problems with Logistic Regressions
            Asked 2021-Jun-12 at 04:41

            BRAND new to ML. Class project has us entering the code below. First I am getting warning:

            ...

            ANSWER

            Answered 2021-Jun-12 at 04:26

            You need to set self.theta to be an array, not a scalar (at least in this specific problem).

            In your case, (intercepted-augmented) X is a '3 by n' array, so try self.theta = [0, 0, 0] for example. This will correct the specific error 'bool' object has no attribute 'mean'. Still, this will just produce preds as a zero vector; you haven't fit the model yet.

            To let you know how I approached the error, I first went to the exact line the error message was pointing to, and put print(preds == y) before the line, and it printed out False. I guess what you expected was a vector of True and Falses. Your y seemed okay; it was a vector (a list to be specific). So I tried print(pred), which showed me a '3 by n' array, which is weird. Now going up from that line, I found out that pred comes from predict_prob(), especially np.dot(X, self.theta). Here, when X is a '3 by n' array and self.theta is a scalar, numpy seems to multiply the scalar to each item in the array and return the array (having the same dimension as the original array), instead of doing matrix multiplication! So you need to explicitly provide self.theta as an array (conforming to the dimension of X).

            Hope the answer and the reasoning behind it helped.

            As for the red line you mentioned in the comment, I guess it is also because you are not fitting the model. (To see the problem, put print(probs) before plt.countour(...). You'll see an array with 0.5 only.)

            So try putting model.fit(X, y) before preds = model.predict(X). (You'll also need to put self.verbose = verbose in the __init__().)

            After that, I get the following:

            Source https://stackoverflow.com/questions/67945325

            QUESTION

            Implementation of Principal Component Analysis from Scratch Orients the Data Differently than scikit-learn
            Asked 2021-Jun-11 at 14:09

            Based on the guide Implementing PCA in Python, by Sebastian Raschka I am building the PCA algorithm from scratch for my research purpose. The class definition is:

            ...

            ANSWER

            Answered 2021-Jun-11 at 12:52

            When calculating an eigenvector you may change its sign and the solution will also be a valid one.

            So any PCA axis can be reversed and the solution will be valid.

            Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.

            Source https://stackoverflow.com/questions/67932137

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

            Vulnerabilities

            No vulnerabilities reported

            Install iris

            You can download it from GitHub.

            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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