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Open Source Articles
Page 119 of 123
Implementing K-means clustering with SciPy by splitting random data in 3 clusters?
Yes, we can also implement a K-means clustering algorithm by splitting the random data in 3 clusters. Let us understand with the example below −Example#importing the required Python libraries: import numpy as np from numpy import vstack, array from numpy.random import rand from scipy.cluster.vq import whiten, kmeans, vq from pylab import plot, show #Random data generation: data = vstack((rand(200, 2) + array([.5, .5]), rand(150, 2))) #Normalizing the data: data = whiten(data) # computing K-Means with K = 3 (3 clusters) centroids, mean_value = kmeans(data, 3) print("Code book :", centroids, "") print("Mean of Euclidean distances :", mean_value.round(4)) ...
Read MoreImplementing K-means clustering with SciPy by splitting random data in 2 clusters?
K-means clustering algorithm, also called flat clustering, is a method of computing the clusters and cluster centers (centroids) in a set of unlabeled data. It iterates until we find the optimal centroid. The clusters, we might think of a group of data points whose inter-point distances are small as compared to the distances to the point outside of that cluster. The number of clusters identified from unlabeled data is represented by ‘K’ in K-means algorithm.Given an initial set of K centers, the K-means clustering algorithm can be done using SciPy library by executing by the following steps −Step1− Data point ...
Read MoreWhich function of scipy.cluster.vq module is used to assign codes from a code book to observations?
Before implementing k-means algorithms, the scipy.cluster.vq.vq(obs, code_book, check_finite = True) used to assign codes to each observation from a code book. It first compares each observation vector in the ‘M’ by ‘N’ obs array with the centroids in the code book. Once compared, it assigns the code to the closest centroid. It requires unit variance features in the obs array, which we can achieve by passing them through the scipy.cluster.vq.whiten(obs, check_finite = True)function.ParametersBelow are given the parameters of the function scipy.cluster.vq.vq(obs, code_book, check_finite = True) −obs− ndarrayIt is an ‘M’ by ‘N’ array where each row is an observation, and ...
Read MoreWhich function of scipy.cluster.vq module is used to normalize observations on each feature dimension?
Before implementing k-means algorithms, it is always beneficial to rescale each feature dimension of the observation set. The function scipy.cluster.vq.whiten(obs, check_finite = True)is used for this purpose. To give it unit variance, it divides each feature dimension of the observation by its standard deviation (SD).ParametersBelow are given the parameters of the function scipy.cluster.vq.whiten(obs, check_finite = True) −obs− ndarrayIt is an array, to be rescaled, where each row is an observation, and the columns are the features seen during each observation. The example is given below −obs = [[ 1., 1., 1.], [ 2., 2., 2.], ...
Read MoreHow can we call the documentation for NumPy and SciPy?
If you are unsure of how to use a particular function or variable in NumPy and SciPy, you can call for the documentation with the help of ‘?’. In Jupyter notebook and IPython shell we can call up the documentation as follows −ExampleIf you want to know NumPy sin () function, you can use the below code −import numpy as np np.sin?OutputWe will get the details about sin() function something like as follows −We can also view the source with the help of double question mark (??) as follows −import numpy as np np.sin??Similarly, if you want to see the ...
Read MoreTo work with SciPy, do I need to import the NumPy functions explicitly?
When SciPy is imported, you do not need to explicitly import the NumPy functions because by default all the NumPy functions are available through SciPy namespace. But as SciPy is built upon the NumPy arrays, we must need to know the basics of NumPy.As most parts of linear algebra deals with vectors and matrices only, let us understand the basic functionalities of NumPy vectors and matrices.Creating NumPy vectors by converting Python array-like objectsLet us understand this with the help of following example−Exampleimport numpy as np list_objects = [10, 20, 30, 40, 50, 60, 70, 80, 90] array_new = np.array(list_objects) print ...
Read MoreHow to add collaborators to a repository in GitHub?
Even if you have a public repository in GitHub, not everyone has the permission to push code into your repository. Other users have a read-only access and cannot modify the repository. In order to allow other individuals to make changes to your repository, you need to invite them to collaborate to the project.The following steps should be performed to invite other team members to collaborate with your repository.Step 1 − Click on the Settings tab in the right corner of the GitHub page.Step 2 − Go to Manage Access option under the Settings tab. On the Manage Access page, you ...
Read MoreHow to create a GitHub repository
A GitHub account is a pre-requisite for creating a GitHub repository. Follow the below steps after registering with GitHub.Step 1 − Login to the GitHub account. Once you login to your account you will see a ‘+’ button on the right. Click on the button and select "New repository" option to create a new repository.Configure the following in the create a new repository page.Repository name: GitHub will validate the repository name that you have entered.Type of the repository: GitHub lets you create the following types of repositories −Private repository − Private Repository is the one that can be accessed only ...
Read MoreHow to clone a GitHub repository?
Cloning a repository involves downloading a copy of the source code from source control. In other words, cloning is creating a copy of an existing repository. Consider an example where multiple users are working on a project. This feature can be used by the users to create a development copy.If you have a GitHub repository, you need to first invite collaborators into the repository. Each collaborator will then clone the repository into their local machines.Locally they will work with this cloned repository, make local changes and perform commits on it. Once they are ready to share their changes with others ...
Read MoreExplain rebasing in Git
Rebasing alters a sequence of commits. It moves or relocates a sequence of commits from current branch to the target branch. By default, the commits from the current branch that are not already on the other branch are rebased. Rebasing technique allows us to keep a linear history.Let us understand from this from the diagram below.To rebase we need to be in the branch which needs to be rebased into the target. In our scenario, we need to execute the rebase command on the feature branch. After executing the rebase command we will get a linear history.After executing the rebase ...
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