Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Programming Articles
Page 2100 of 2547
Find the number of different numbers in the array after applying the given operation q times in C++
In this problem, we are given a number N which is the size of an array consisting of all zeros and Q queries each of the following type −update(s, e, val ) -> this query will update all elements from s to e (both inclusive) to val.Our task is to find the number of different numbers in the array after applying the given operation q timesLet’s take an example to understand the problem, Input : N = 6, Q = 2 Q1 = update(1, 4, 3) Q2 = update(0, 2, 4) Output : 2ExplanationInitial array, arr[] = {0, 0, ...
Read MoreFind the number of boxes to be removed in C++
In this problem, we are given an array arr[] in which each element represents a pile of boxes (each of unit height). Our task is to find the number of boxes to be removed.The person is standing at index 0 of the array at the height of the pile of boxes and he needs to move to the end of the array. The condition to move from one pile to the next is by jumping to the next.Jump is possible only when the next pile is at the same height or is at height less than it. If the height ...
Read MoreFind the Nth term of the series 9, 45, 243,1377...in C++
In this problem, we are given an integer value N.Our task is to Find the nth term of the series −9, 45, 243, 1377, 8019, …Let’s take an example to understand the problem,Input : N = 4 Output : 1377Solution ApproachA simple solution to find the problem is by finding the Nth term using observation technique. On observing the series, we can formulate as follow −(11 + 21)*31 + (12 + 22)*32 + (13 + 23)*33 … + (1n + 2n)*3nExampleProgram to illustrate the working of our solution#include #include using namespace std; long findNthTermSeries(int n){ return ( ( (pow(1, n) + pow(2, n)) )*pow(3, n) ); } int main(){ int n = 4; cout
Read MoreFind the nth term of the series 0, 8, 64, 216, 512,... in C++
In this problem, we are given an integer value N. Our task is to find the nth term of the series −0, 8, 64, 216, 512, 1000, 1728, 2744…Let’s take an example to understand the problem, Input: N = 6 Output: 1000Solution ApproachTo find the Nth term of the series, we need to closely observe the series. The series is the cube of even numbers, where the first term is 0.So, the series can be decoded as −[0]3, [2]3, [4]3, [6]3, [8]3, [10]3…For ith term, T1 = [0]3 = [2*(1-1)]3T2 = [2]3 = [2*(2-1)]3T3 = [4]3 = [2*(3-1)]3T4 = [6]3 ...
Read MoreFind the nth term of the given series 0, 0, 2, 1, 4, 2, 6, 3, 8, 4... in C++
In this problem, we are given an integer value N. Our task is to Find the nth term of the given series −0, 0, 2, 1, 4, 2, 6, 3, 8, 4, 10, 5, 12, 6, 14, 7, 16, 8, 18, 9, 20, 10… Let’s take an example to understand the problem, Input − N = 6 Output − 2Solution ApproachTo find the Nth term of the series, we need to closely observe the series. It is the mixture of two series and odd and even terms of the series. Let’s see each of them, At even positions −T(2) = 0T(4) ...
Read MoreHow to upsample a given multi-channel temporal, spatial or volumetric data in PyTorch?
A temporal data can be represented as a 1D tensor, and spatial data as 2D tensor while a volumetric data can be represented as a 3D tensor. The Upsample class provided by torch.nn module supports these types of data to be upsampled. But these data must be in the form N ☓ C ☓ D (optional) ☓ H (optional) ☓ W (optional), Where N is the minibatch size, C is the numberchannels, D, H and W are depth, height and width of the data, respectively. Hence, to upsample a temporal data (1D), we need it to be in 3D in ...
Read MoreHow to adjust saturation of an image in PyTorch?
The saturation of an image refers to the intensity of a color. The higher the saturation of a color, the more vivid it is. The lower the saturation of a color, the closer it is to gray.To adjust the saturation of an image, we apply adjust_saturation(). It's one of the functional transforms provided by the torchvision.transforms module. adjust_saturation() transformation accepts both PIL and tensor images. A tensor image is a PyTorch tensor with shape [C, H, W], where C is the number of channels, H is the image height, and W is the image width.This transform also accepts a batch ...
Read MoreHow to apply linear transformation to the input data in PyTorch?
We can apply a linear transformation to the input data using the torch.nn.Linear() module. It supports input data of type TensorFloat32. This is applied as a layer in the deep neural networks to perform linear transformation. The linear transform used −y = x * W ^ T + bHere x is the input data, y is the output data after linear transform. W is the weight matrix and b is biases. The weights W have shape (out_features, in_features) and biases b have shape (out_features). They are initialized randomly and updated during the training of a Neural Network.Syntaxtorch.nn.Linear(in_features, out_features)Parametersin_features - It ...
Read MoreHow to flatten an input tensor by reshaping it in PyTorch?
A tensor can be flattened into a one-dimensional tensor by reshaping it using the method torch.flatten(). This method supports both real and complex-valued input tensors. It takes a torch tensor as its input and returns a torch tensor flattened into one dimension.It takes two optional parameters, start_dim and end_dim. If these parameters are passed, only those dimensions starting with start_dim and ending with end_dim are flattened.The order of elements in the input tensor is not changed. This function may return the original object, a view, or copy. In the following examples, we cover all the aspects of flattening the tensor ...
Read MoreHow to compute the cross entropy loss between input and target tensors in PyTorch?
To compute the cross entropy loss between the input and target (predicted and actual) values, we apply the function CrossEntropyLoss(). It is accessed from the torch.nn module. It creates a criterion that measures the cross entropy loss. It is a type of loss function provided by the torch.nn module.The loss functions are used to optimize a deep neural network by minimizing the loss. CrossEntropyLoss() is very useful in training multiclass classification problems. The input is expected to contain unnormalized scores for each class.The target tensor may contain class indices in the range of [0, C-1] where C is the number ...
Read More