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To compute the Heaviside step function for each element in the input tensor, we use the torch.heaviside() method. It accepts two parameters − input and values. It returns a new tensor with a computed heaviside step function.The value of heaviside function is the same as values if input=0. The value of heaviside is zero if input is less than zero. The value of heaviside is 1 if input is greater than zero. It accepts torch tensors of any dimension. It is also called the unit step function.Syntaxtorch.heaviside(input, values)StepsWe could use the following steps to compute the Heaviside step function −Import ... Read More
To draw binary random numbers (0 or 1) from a Bernoulli distribution, we apply the torch.bernoulli() method. The input to this method is a torch tensor containing the probabilities of drawing 1. These probabilities are used to draw binary random numbers (0 or 1).As the input tensor contains the probabilities, all the elements should be in the range [0, 1]. It returns a tensor whose elements (0 or 1) are randomly selected from a Bernoulli distribution with the input probabilities.Syntaxtorch.bernoulli(input)where, the parameter input is a torch tensor containing the probabilities of drawing 1. These probabilities are used to draw the ... Read More
To create a tensor of random numbers drawn from separate normal distributions whose mean and std are given, we apply the torch.normal() method. This method takes two input parameters − mean and std.mean is a tensor with the mean of each output element’s normal distribution, andstd is a tensor with the standard deviation of each output element’s normal distribution.It returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are mean and std.Syntaxtorch.normal(mean, std)StepsWe could use the following steps to create a tensor of random numbers drawn from separate normal distributions −Import the required ... Read More
With given absolute values and angles, we can construct a complex number in PyTorch using torch.polar() method. The absolute value and angles must be float or double. Both the absolute value and the angle must be of the same type.If abs is a float, then angle must also be float.If the inputs are torch.float32, then the constructed complex tensor must be torch.complex64.If the inputs are torch.float64, then the complex tensor must be torch.complex128.Syntaxtorch.polar(abs, angle)Parametersabs – The absolute length of the complex tensor.angle – The angle of the complex tensor.StepsWe could use the following steps to construct a complex tensor with ... Read More
The hessian() function computes the Hessian of a given function. The hessian() function can be accessed from the torch.autograd.functional module. The function whose Hessian is being computed takes a tensor as the input and returns a tuple of tensors or a tensor. The hessian() function returns a tensor with the Hessian values computed for a function with the given input.Syntaxtorch.autograd.functional.hessian(func, input)Parametersfunc − It's a Python function for which the Hessian is computed.input − It’s input to the function, func.StepsWe could use the following steps to compute the Hessian of a given function −Import the required library. In all the following ... Read More
The jacobian() function computes the Jacobian of a given function. The jacobian() function can be accessed from the torch.autograd.functional module. The function whose Jacobian is being computed takes a tensor as the input and returns a tuple of tensors or a tensor. The jacobian() function returns a tensor with Jacobian values computed for a function with the given input.Syntaxtorch.autograd.functional.jacobian(func, input)Parametersfunc − It's a Python function for which the Jacobian is computed.input − It’s the input to the function, func.StepsWe could use the following steps to compute the Jacobian of a given function −Import the required library. In all the following ... Read More
In this problem, we are given an array arr[] consisting of n integer values. Our task is to find the sum of all unique subarray sum for a given array. Subarray sum is the sum of elements of the given subarray.Let's take an example to understand the problem, Input : arr[] = {1, 2, 4} Output : 23Explanation −All subarrays of the given array are : (1), (2), (4), (1, 2), (2, 4), (1, 2, 4) Sum of subarrays = 1 + 2 + 4 + (1+2) + (2+4) + (1+2+4) = 23Solution ApproachA solution to the problem is by ... Read More
In this problem, we are given a binary tree. Our task is to find the sum of all left right in a given Binary Tree.Let's take an example to understand the problem, Input :Output : 8Explanation −All leaf nodes of the tree are : 1, 8 Sum = 1 + 8 = 9Solution ApproachA simple solution to the problem is traversing the tree from root to leaf. If a node is a left leaf node, add it to sum. When the whole tree is traversed. Print Sum.ExampleProgram to illustrate the working of our solution#include using namespace std; struct Node{ ... Read More
In this problem, we are given a binary tree. Our task is to find the sum of all left leaves in a given Binary Tree.Let's take an example to understand the problem, Input : Output : 11Explanation −All leaf nodes of the tree are : 2, 9 Sum = 2 + 9 = 11Solution ApproachA simple solution to the problem is traversing the tree from root to leaf. If a node is a left leaf node, add it to sum. When the whole tree is traversed. Print Sum.ExampleProgram to illustrate the working of our solution#include using namespace std; struct Node{ ... Read More
In this problem, we are given an array arr[] consisting of N integers stored in unsorted order. Our task is to find a subarray with a given sum.Let's take an example to understand the problem, Input : arr[] = {2, 5, -1, 4, 6, -9, 5} sum = 14 Output : subarray = {5, -1, 4, 6}Explanation −Subarray sum = 5 - 1 + 4 + 6 = 14Solution ApproachA simple solution to the problem is using nested loops. We will loop through the array and using an inner loop, we will find subarray. For each subarray we will find ... Read More