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I'm working on cross_validation to choose hyperparameters,and I split my training data into k folds.Take one folder as my validation data,the others as training data(I will do that for k times in fact).
X_train_folds = numpy.array_split(X_train,k)
The X_tran_folds will look like this:[subarray1,subarray2,.....]
The subarrays have the same number of columns.
But how to merge the k-1 subarrays into one?
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Because the Numpy array is densely packed in memory due to its homogeneous type, it also frees the memory faster. So overall a task executed in Numpy is around 5 to 100 times faster than the standard python list, which is a significant leap in terms of speed.
Splitting NumPy Arrays Splitting is reverse operation of Joining. Joining merges multiple arrays into one and Splitting breaks one array into multiple. We use array_split() for splitting arrays, we pass it the array we want to split and the number of splits.
Inverse of a Matrix using NumPy Python provides a very easy method to calculate the inverse of a matrix. The function numpy. linalg. inv() which is available in the python NumPy module is used to compute the inverse of a matrix.
Therefore, we can save the NumPy arrays into a native binary format that is efficient to both save and load. This is common for input data that has been prepared, such as transformed data, that will need to be used as the basis for testing a range of machine learning models in the future or running many experiments.
You can use numpy.concatenate() to join a sequence of arrays:
>>> import numpy as np
>>> a = np.array([0, 1])
>>> b = np.array([2, 3])
>>> np.concatenate((a, b))
array([0, 1, 2, 3])
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