Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Hamming distance calculates the distance between two binary vectors. Mostly we find the binary strings when we use one-hot encoding on categorical columns of data. In one-hot encoding the integer variable is removed and a new binary variable will be added for each unique integer value. For example, if a column had the categories say ‘Length’, ‘Width’, and ‘Breadth’. We might one-hot encode each example as a bitstring with one bit for each column as follows −Length = [1, 0, 0]Width = [0, 1, 0]Breadth = [0, 0, 1]The Hamming distance between any of the two categories mentioned above, can ... Read More
The EM (Expectation-Maximization) algorithm is a famous iterative refinement algorithm that can be used for discovering parameter estimates. It can be considered as an extension of the k-means paradigm, which creates an object to the cluster with which it is most similar, depending on the cluster mean.EM creates each object to a cluster according to a weight defining the probability of membership. In other term, there are no strict boundaries among clusters. Thus, new means are evaluated based on weighted measures.EM begins with an original estimate or “guess” of the parameters of the combination model (collectively defined as the parameter ... Read More
It is difficult to remember the values, units, and precisions of all physical constants. That’s the reason scipy.constants() have four methods with the help of which we can access physical constants. Let’s understand these methods along with examples −scipy.constants.value(key)− This method will give us the value in physical constants indexed by key.Parameterskey- It represents the key in dictionary physical_constants. Its value is a Python string or Unicode.Returnsvalue- It represents the value in physical_constants corresponding to the key parameter. Its value is of float type.Examplefrom scipy import constants constants.value(u'proton mass')Output1.67262192369e-27scipy.constants.unit(key)− This method will give us the unit in physical constants indexed ... Read More
To implement Scientific or Mathematical calculation, we need various universal constants. For example, the formula to calculate area of a circle is pi*r*r where Pi is a constant having value = 3.141592653. There are various other scenarios like this where we need constants. It would really be helpful if we can incorporate these constants into our calculation with ease. The scipy.constants(), a sub-module inside the Scipy library, does the job for us and provide us a reference material to look up exhaustive list of Physical Constants, universal mathematical constants, and various units such as SI prefixes, Binary prefixes, Mass, Angle, ... Read More
The scipy.cluster.vq()has two methods to implement k-means clustering namely kmeans() and kmeans2(). There is a significant difference in the working of both these methods. Let us understand it −scipy.cluster.vq.kmeans(obs, k_or_guess, iter=20, thresh=1e-05, check_finite=True)− The kmeans() method forms k clusters by performing k-means algorithm on a set of observation vectors. To determine the stability of the centroids, this method uses a threshold value to compare the change in average Euclidean distance between the observations and their corresponding centroids. The output of this method is a code book mapping centroid to codes and vice versa.scipy.cluster.vq.kmeans2(data, k, iter=10, thresh=1e-05, minit='random', missing='warn', check_finite=True)− The ... Read More
scipy.cluster.vq.kmeans2(data, k, iter=10, thresh=1e-05, minit='random', missing='warn', check_finite=True)− The kmeans2() method classify a set of observations vectors into k clusters by performing k-means algorithm. To check for convergence, the kmeans2() method does not use threshold values. It has additional parameters to decide the method of initialization of centroids, to handle empty clusters, and to validate if the input metrices contain only finite numbers or not.Below is given the detailed explanation of its parameters −Parametersdata− ndarrayIt is an ‘M’ by ‘N’ array of M observations in N dimension.k− int or ndarrayThis parameter represents the number of clusters to form and the centroids ... Read More
The scipy.cluster.vq.kmeans(obs, k_or_guess, iter=20, thresh=1e- 05, check_finite=True)method forms k clusters by performing a k-means algorithm on a set of observation vectors. To determine the stability of the centroids, this method uses a threshold value to compare the change in average Euclidean distance between the observations and their corresponding centroids. The output of this method is a code book mapping centroid to codes and vice versa.Below is given the detailed explanation of its parameters−Parametersobs− ndarrayIt is an ‘M’ by ‘N’ array where each row is an observation, and the columns are the features seen during each observation. Before using, these features ... Read More
In this article, we need to find a number of prefix sum which are prime numbers in a given array arr[ ] of positive integers and range query L, R, where L is the initial index value arr[ L ] for prefixsum[ ] array and R is the number of prefix sum we need to find.To fill the prefix sum array, we start with index L to index R and add the present value with the last element in the given array. So here is the Example for the problem −Input : arr[ ] = { 3, 5, 6, 2, ... Read More
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 More
In this article, we will explain how to solve the number of possible pairs of hypotenuse and area form a right-angled triangle in C++.We need to determine the number of all possible pairs of a hypotenuse and the area ( H, A ) to form a right-angled triangle with H as hypotenuse and A as Area.In this example − x = Base of Right Angled Triangle y = Height of Right Angled Triangle H = hypotenuse of Right Angled TriangleWe know Area of right angled triangle, A = ( x * ... Read More