Iris | modern shaders mod for Minecraft | Video Game library

 by   IrisShaders Java Version: 1.6.4+1.20 License: LGPL-3.0

kandi X-RAY | Iris Summary

kandi X-RAY | Iris Summary

Iris is a Java library typically used in Gaming, Video Game, Minecraft applications. Iris has no bugs, it has no vulnerabilities, it has a Weak Copyleft License and it has medium support. However Iris build file is not available. You can download it from GitHub.

(WIP) A modern shaders mod for Minecraft intended to be compatible with existing OptiFine shader packs

            kandi-support Support

              Iris has a medium active ecosystem.
              It has 2746 star(s) with 542 fork(s). There are 50 watchers for this library.
              It had no major release in the last 12 months.
              There are 188 open issues and 1160 have been closed. On average issues are closed in 11 days. There are 5 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Iris is 1.6.4+1.20

            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 is licensed under the LGPL-3.0 License. This license is Weak Copyleft.
              Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

            kandi-Reuse Reuse

              Iris releases are available to install and integrate.
              Iris has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Iris and discovered the below as its top functions. This is intended to give you an instant insight into Iris implemented functionality, and help decide if they suit your requirements.
            • Determine the intersection of a sphere
            • Test whether a point is inside a triangle
            • Computes the lowest root root of the quadratic equation
            • Determines the intersection of a sphere
            • Computes the lowest root of the quadratic
            • Rotates the given object matrix
            • Apply the transformation of an object to a sphere
            • Performs the actual transformation on the AST
            • Converts a shader type to an array of patches
            • Finds the closest points on a line segment
            • Returns the closest point on the two line segments
            • Compute the perspective transformation
            • Computes the perspective transformation
            • Rotates a cylindrical matrix
            • Compute the direction of the frustum
            • Computes the direction of the frustum plane
            • Test if an intersecting box
            • Computes the weighted average of all the quaternions
            • Finds the closest point on a line segment
            • Test to see if a sphere is found
            • Computes and returns a projection matrix that covers the specified plane
            • Transforms each AST node in a VERT declaration
            • Computes the weighted average of all quaternions in the given order
            • Computes the projected grid range
            • Updates the frustum rectangle with the given matrix
            • Creates a custom texture
            Get all kandi verified functions for this library.

            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


            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



            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 -



            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



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



            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:



            Answered 2021-Jun-14 at 07:45

            You can use the following -



            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:



            Answered 2021-Jun-14 at 16:10

            We can use across (used dplyr 1.0.6 version)



            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



            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:



            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?



            Answered 2021-Jun-14 at 14:52


            • 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:



            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:



            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:



            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.



            Answered 2021-Jun-12 at 09:19

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



            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:



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

            After that, I get the following:



            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:



            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.


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


            No vulnerabilities reported

            Install Iris

            You can download it from GitHub.
            You can use Iris 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 Iris 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 For Gradle installation, please refer .


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