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Tag: arrays

FAST: 1D overlaps with rows in 2D?

let say i have 2D array, f.e.: I want to calculate overlap with 1D vector, FAST. I can almost do it with (8ms on big array): The problem with it is that it only matches if both Position and Value match. F.e. 5 in 2nd column of 1d vec did not match with 5 in 3rd column on the 2nd

Manipulating numpy arrays (concatenating inner sub-arrays)

I have a question of manipulating numpy arrays. Say, given a 3-d array in the form np.array([[[1,2],[3,4]], [[5,6],[7,8]]]) which is a (2,2,2) array. I want to manipulate it into a (2,4) array such that a = np.array([[1,2,5,6],[3,4,7,8]]). I want to know is there any built-in methods of numpy particularly dealing with problems like this and can be easily generalized. EDITED:

GaussianProcessRegressor ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size

I am running the following code: The shape of my input is: (19142, 21) dtypes are each: float64 Added in Edit: X and y are Pandas Dataframes. After .values they’re each numpy arrays And I get the Error: ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size. I cant image a dataset of 20000

Calculate squared deviation from the mean for each element in array

I have an array with shape (128,116,116,1), where 1st dimension asthe number of subjects, with the 2nd and 3rd being the data. I was trying to calculate the variance (squared deviation from the mean) at each position (i.e: in (0,0), (0,1), (1,0), etc… until (116,116)) for all the 128 subjects, resulting in an array with shape (116,116). Can anyone tell

Remove and add values to ​numpy array

Is there a more efficient way to remove the 0 from the beginning and insert the 20 at the end and retain the shape (1, 20)? Output: Answer You can just select a subset of the current array excluding the first element and then add 20 or whatever scalar you want at the end.

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