Weka jar file has the correct path install#

Posted in Comp Sci | Tagged High DPI, Java, scaling Generating a SSH key and uploading it to a server Oh! change the uiScale factor to suite your preference.

Make sure you have the Latest Java installed (IвЂ™m using Java9 straight from Oracle). Icon=/home/mike/Programs/weka-3-8-1/weka.ico IвЂ™ve put my weka jar file in a directory called Programs in my home folder.Д®xec=java =3.5 -jar /home/mike/Programs/weka-3-8-1/weka.jar

IвЂ™m using Fedora (and gnome) so create a Desktop file for Weka with this pasth and filename: ~/.local/share/applications/sktop When I first ran it from the command line it looked like a tiny little thing in the top left of the screen however a small amount of DuckDuckGo-ing resulted in the correct way of dealing with it.

Weka jar file has the correct path install#

Next, run the following command on your Linux terminal to set the path for the gridstore.IвЂ™ve recently bought the developer edition of the XPS13 with a highDPI screen and needed to install WEKA for a course IвЂ™m doing. java file to the bin folder of your GridDB located in the following path: Compile and Run the CodeДЇirst, login as the gsadm user. In our case, we have passed a value of 1, so we will predict 1 nearest neighbor for the customer. If you donвЂ™t pass any argument and you call it with the default constructor, it will calculate 1 nearest neighbor. If you pass 2, it will calcute 2 nearest neighbors. If you pass to it a value of 1, it will find 1 nearest neighbor. The IBk instance takes an integer argument.

We specified the value of k when creating the IBk instance. Res.setClassIndex(res.numAttributes() - 1) Д®valuation evaluation = new Evaluation(res) LetвЂ™s first import the packages that we will need to use: The data will then be pulled from GridDB for analysis with the algorithm Import Packages We will read the data from the csv file and load into GridDB. The dataset has been saved in a CSV file named customers.csv. We now want to implement the KNN algorithm in java using the above dataset. Implementing K-Nearest Neighbors Algorithm in Java If k is 1, it will show 1 product, that is, 1 nearest neighbor, while if k is 2, it will show 2 products, that is, 2 nearest neighbors. We will first calculate the distance and then depending on the k value, we will be able to get the nearest k neighbors.Д«y default, the value of k is 1, but we can pass the value of k during the creation of the instance. We will find the recommendations for a customer with an age of 58 years and an income of 51000. The dataset to be used shows the age, income, and the product bought by a customer as shown below: We will use a customer dataset to make a prediction for unknown data. The KNN algorithm is referred to as a lazy learning algorithm because it does not learn a discriminative function from the training dataset but it instead memorizes the training dataset. The class with the highest probability is chosen. It then calculates the probability of the test data falling to the K classes. The algorithm selects K number of points which are closest to the test data. KNN predicts the correct class for the test data by determining the distance between the test data and the training points. K-nearest neighbors (KNN) is a supervised machine learning algorithm used to perform classification and regression tasks.