slice

问题 Suppose I have an array with 10 elements, e.g. a=np.arange(10) . If I want to create another array with the 1st, 3rd, 5th, 7th, 9th, 10th elements of the original array, i.e. b=np.array([0,2,4,6,8,9]) , how can I do it efficiently? thanks 回答1: a[[0, 2, 4, 6, 8, 9]] Index a with a list or array representing the desired indices. (Not 1, 3, 5, 7, 9, 10 , because indexing starts from 0.) It's a bit confusing that the indices and the values are the same here, so have a different example: >>> a =

问题 I have a large 2d numpy array and I want to remove subsets of it and handle what remains to a function. I need to do this for many subsets, and thus I would ideally not want to create a copy of the array each time. The function doesn't change any values in the array. mat = np.load(filename) mat_1 = mat[:i,:] mat_2 = mat[j:,:] So far, mat_1 and mat_2 are views. Then I would like to do mat_s = np.concatenate((mat_1,mat_2)) result = func(mat_s) but without making a copy. Is this possible? 回答1:

问题 I have a large 2d numpy array and I want to remove subsets of it and handle what remains to a function. I need to do this for many subsets, and thus I would ideally not want to create a copy of the array each time. The function doesn't change any values in the array. mat = np.load(filename) mat_1 = mat[:i,:] mat_2 = mat[j:,:] So far, mat_1 and mat_2 are views. Then I would like to do mat_s = np.concatenate((mat_1,mat_2)) result = func(mat_s) but without making a copy. Is this possible? 回答1:

问题 I have a large 2d numpy array and I want to remove subsets of it and handle what remains to a function. I need to do this for many subsets, and thus I would ideally not want to create a copy of the array each time. The function doesn't change any values in the array. mat = np.load(filename) mat_1 = mat[:i,:] mat_2 = mat[j:,:] So far, mat_1 and mat_2 are views. Then I would like to do mat_s = np.concatenate((mat_1,mat_2)) result = func(mat_s) but without making a copy. Is this possible? 回答1:

问题 Is there a way of elegantly specify a slice in a multidimensional array in R, whereby the number of dimensions is not known beforehand? For instance, for a 5-dimensional array, slicing out the last dimension can be done using fourdimslice <- fivedimarray[,,,,1] but I'l like to code this for arbitrary dimensions, something like slice <- arbitrarydimarray(dim=5, index=1) I've not been able to understand if do.call('[', ...) would be an approach. I've also tried named dimensions, but a[fifth=1]

问题 Suppose that I have arrays A and B in Go. What is the fastest way to append all the values of B to A ? 回答1: Arrays in Go are secondary, slices are the way to go. Go provides a built-in append() function to append slices: a := []int{1, 2, 3} b := []int{4, 5} a = append(a, b...) fmt.Println(a) Output: [1 2 3 4 5] Try it on the Go Playground. Notes: Arrays in Go are fixed sizes: once an array is created, you cannot increase its size so you can't append elements to it. If you would have to, you

问题 Suppose that I have arrays A and B in Go. What is the fastest way to append all the values of B to A ? 回答1: Arrays in Go are secondary, slices are the way to go. Go provides a built-in append() function to append slices: a := []int{1, 2, 3} b := []int{4, 5} a = append(a, b...) fmt.Println(a) Output: [1 2 3 4 5] Try it on the Go Playground. Notes: Arrays in Go are fixed sizes: once an array is created, you cannot increase its size so you can't append elements to it. If you would have to, you

问题 The capacity parameter in making a slice in Go does not make much sense to me. For example, aSlice := make([]int, 2, 2) //a new slice with length and cap both set to 2 aSlice = append(aSlice, 1, 2, 3, 4, 5) //append integers 1 through 5 fmt.Println("aSlice is: ", aSlice) //output [0, 0, 1, 2, 3, 4, 5] If the slice allows inserting more elements than the capacity allows, why do we need to set it in the make() function? 回答1: The builtin append() function uses the specified slice to append

问题 I want to perform slicing in Nd4j of arbitrary sizes in the same manner as I am able to do using Numpy. a = numpy.arange(100) a[25:50] The nd4j slice method only takes dimension and index arguments, not length. How can I achieve this? 回答1: I know it's an old question but I ran across it while googling this problem exactly. By inspecting the source code for slice I believe it can only return full rows/columns not partial from index to index. You can use method get with an NDArrayIndex instance

问题 I have something like a np.arange([100000]) and i need to retrieve data between two indexes multiple times. Currently i running this which is slow data = np.arange([100000]) # This array usually contains thousands of slices slices = np.array( [ [1, 4], [10,20], [100,110], [1000,1220] ]) # One way i have been doing it np.take(data, [i for iin, iout in slices for idx in range(iin, iout)]) # The other way [data[iin:iout] for iin, iout in slices] Both ways are slow. I need this to be very fast. I