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A quadrilateral forms a polygon with four vertices and four edges in Euclidean plane geometry. The name 4-gon etc. Included in other names of quadrilaterals and sometimes they are also known as a square, display style, etc.In this article, we will explain the approaches to finding the number of quadrilaterals possible from the given points. In this problem, we need to find out how many possible quadrilaterals are possible to create with the provided four points ( x, y ) in the cartesian plane. So here is the example for the given problem −Input : A( -2, 8 ), B( ... Read More
Below python script will compare the ‘cubic’ and ‘linear’ interpolation on same data using SciPy library −ExampleFirst let’s generate some data to implement interpolation on that −import numpy as np from scipy.interpolate import interp1d import matplotlib.pyplot as plt A = np.linspace(0, 10, num=11, endpoint=True) B = np.cos(-A**2/9.0) print (A, B)OutputThe above script will generate the following points between 0 and 4 − [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.] [ 1. 0.99383351 0.90284967 0.54030231 -0.20550672 -0.93454613 -0.65364362 0.6683999 0.67640492 -0.91113026 0.11527995]Now, let’s plot these points as follows −plt.plot(A, B, '.') plt.show()Now, based on fixed data ... Read More
In this article we will describe the ways to find the number of primes in a subarray. We have an array of positive numbers arr[] and q queries having two integers that denote our range {l, R} we need to find the number of primes in the given range. So below is an example of the given problem −Input : arr[] = {1, 2, 3, 4, 5, 6}, q = 1, L = 0, R = 3 Output : 2 In the given range the primes are {2, 3}. Input : arr[] = {2, 3, 5, 8 ... Read More
To implement ‘cubic’ 1-D interpolation using SciPy, we need to specify the kind of interpolation as ‘cubic’ in the ‘kind’ parameter of scipy.interpolate.interp1d class. Let’s see the example below to understand it−ExampleFirst let’s generate some data to implement interpolation on that −import numpy as np from scipy.interpolate import interp1d import matplotlib.pyplot as plt A = np.linspace(0, 10, num=11, endpoint=True) B = np.cos(-A**2/9.0) print (A, B)OutputThe above script will generate the following points between 0 and 4 − [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.] [ 1. 0.99383351 0.90284967 0.54030231 -0.20550672 -0.93454613 -0.65364362 0.6683999 0.67640492 -0.91113026 ... Read More
In this article, we will explain everything about finding the number of prime pairs in an array using C++. We have an array arr[] of integers, and we need to find all the possible prime pairs present in it. So here is the example for the problem −Input : arr[ ] = { 1, 2, 3, 5, 7, 9 } Output : 6 From the given array, prime pairs are (2, 3), (2, 5), (2, 7), (3, 5), (3, 7), (5, 7) Input : arr[] = {1, 4, 5, 9, 11} Output : 1Approaches to Find ... Read More
Interpolation is a method of generating a value between two given points on a line or a curve. In machine learning, interpolation is used to substitute the missing values in a dataset. This method of filling the missing values is called imputation. Another important use of interpolation is to smooth the discrete points in a dataset.SciPy provides us a module named scipy.interpolate having many functions with the help of which we can implement interpolation.ExampleIn the below example we will implement Interpolation by using the scipy.interpolate() package −First let’s generate some data to implement interpolation on that −import numpy as np ... Read More
Semi-supervised clustering is a method that partitions unlabeled data by creating the use of domain knowledge. It is generally expressed as pairwise constraints between instances or just as an additional set of labeled instances.The quality of unsupervised clustering can be essentially improved using some weak structure of supervision, for instance, in the form of pairwise constraints (i.e., pairs of objects labeled as belonging to similar or different clusters). Such a clustering procedure that depends on user feedback or guidance constraints is known as semisupervised clustering.There are several methods for semi-supervised clustering that can be divided into two classes which are ... Read More
Constraint-based clustering finds clusters that satisfy user-stated preferences or constraints. It is based on the nature of the constraints, constraint-based clustering can adopt instead of different approaches. There are several categories of constraints which are as follows −Constraints on individual objects − It can define constraints on the objects to be clustered. In a real estate application, for instance, one can like to spatially cluster only those luxury mansions worth over a million dollars. This constraint confines the collection of objects to be clustered. It can simply be managed by preprocessing (e.g., implementing selection using an SQL query), after which ... Read More
The determinant of a matrix, denoted by |A|, is a scalar value that can be calculated from a square matrix. With the help of the determinant of a matrix, we can find the inverse of a matrix and other things that are useful in the systems of linear equations, calculus, etc. The function named scipy.linalg.det() calculates the determinant of a square matrix.Let’s understand it with the below given examples −ExampleCalculating determinant of 2 by 2 matrix#Importing the scipy package import scipy #Importing the numpy package import numpy as np #Declaring the numpy array (Square Matrix) X = np.array([[5, ... Read More
SciPy has a function called scipy.linalg.solve() to solve linear equations. All we need to know is how we can represent our linear equation in terms of vectors. It will solve the linear equation set a * x = b for the unknown x. Let’s understand it with the help of below example −ExampleIn this example, we will be trying to solve a linear algebra system which can be given as follows − 3x + 2y = 2 x - y = 4 5y + z = -1The function scipy.linalg.solve() will find the values of x, y, and z for which ... Read More