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To find the sum of corresponding elements in all matrix’s stored in a list in R, we can use Reduce function with plus sign. For Example, if we have a list called LIST that contains multiple matrices and we want to find the sum of corresponding elements then we can use the command given below −Reduce("+",LIST)Check out the below Examples to understand how it works.Example 1To find the sum of corresponding elements in all matrix’s stored in a list in R, use the following snippet −List
To find the sum of all array elements in R, we can use Reduce function with plus sign. For Example, if we have an array called ARRAY and we want to find the sum of all values in this array then we can use the command Reduce("+",ARRAY).Check out the below Examples to understand how it works.Example 1To find the sum of all array elements in R use the snippet given below −Array1
To extract a data frame column values as a vector by matching a vector in R, we can use subsetting with %in% operator.For Example, if we have a data frame called df having a column say C and a vector V and we want to extract values in C if they match with V then we can use the command given below −df[df$C %in% V,"C"]Example 1Following snippet creates a sample data frame and vector −x1
Symbol Table is a data structure that supports an effective and efficient way of storing data about various names occurring in the source code. These names are used in the source code to identify the different program elements, like a variable, constants, procedures, and the labels of statements.The symbol table is searched each time a name is encountered in the source text. When a new name or new data about an existing name is found, the content of the symbol table modifies. Thus, the symbol table should have an effective structure for creating the data held in the table also ... Read More
To compute the sine of elements of a tensor, we use the torch.sin() method. It returns a new tensor with the sine values of the elements of the original input tensor. It takes a tensor as the input parameter and outputs a tensor.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Create a tensor and print it.Compute torch.sin(input). It takes input, a tensor as input parameter, and returns a new tensor with the sine values of elements of the input.Print the tensor with the sine values of ... Read More
To compare two tensors element-wise in PyTorch, we use the torch.eq() method. It compares the corresponding elements and returns "True" if the two elements are same, else it returns "False". We can compare two tensors with same or different dimensions, but the size of both the tensors must match at non-singleton dimension.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Create a PyTorch tensor and print it.Compute torch.eq(input1, input2). It returns a tensor of "True" and/or "False". It compares the tensor element-wise, and returns True if the corresponding ... Read More
To sort the elements of a tensor in PyTorch, we can use the torch.sort() method. This method returns two tensors. The first tensor is a tensor with sorted values of the elements and the second tensor is a tensor of indices of elements in the original tensor. We can compute the 2D tensors, row-wise and column-wise.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Create a PyTorch tensor and print it.To sort the elements of the above-created tensor, compute torch.sort(input, dim). Assign this value to a new ... Read More
To compute the logarithm of elements of a tensor in PyTorch, we use the torch.log() method. It returns a new tensor with the natural logarithm values of the elements of the original input tensor. It takes a tensor as the input parameter and outputs a tensor.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Create a tensor and print it.Compute torch.log(input). It takes input, a tensor, as the input parameter and returns a new tensor with the natural logarithm values of elements of the input.Print the tensor ... Read More
A PyTorch tensor is homogenous, i.e., all the elements of a tensor are of the same data type. We can access the data type of a tensor using the ".dtype" attribute of the tensor. It returns the data type of the tensor.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.Create a tensor and print it.Compute T.dtype. Here T is the tensor of which we want to get the data type.Print the data type of the tensor.Example 1The following Python program shows how to get the data ... Read More
To squeeze a tensor, we use the torch.squeeze() method. It returns a new tensor with all the dimensions of the input tensor but removes size 1. For example, if the shape of the input tensor is (M ☓ 1 ☓ N ☓ 1 ☓ P), then the squeezed tensor will have the shape (M ☓ M ☓ P).To unsqueeze a tensor, we use the torch.unsqueeze() method. It returns a new tensor dimension of size 1 inserted at specific position.StepsImport the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed ... Read More