If an int, the random sample is generated as if a was np. Examples > np.random.permutation(10) array ( 1, 7, 4, 3, 0, 9, 2, 5, 8, 6) > np.random. If you want to split the data set once in two parts, you can use, or if you need to keep track of the indices (remember to fix the random seed to make everything reproducible). If x is a multi-dimensional array, it is only shuffled along its first index. If an ndarray, a random sample is generated from its elements. Randomly permute a sequence, or return a permuted range. If you want to shuffle all the elements along all axises you can do this np.random.permutation(arr.flatten()). Generates a random sample from a given 1-D array. Then, you can apply the permutation using the take() method: arr.take(sampler, axis = 1) You can define the sampler as follows: sampler = np.random.permutation(5) However, if you have a multi-dimensional array, you can use the following code to perform the permutation along a specific axis: sampler = np.random.permutation(4) # Size of the selected axisĭf.take(sampler, axis=0) # You can select your desired axis from hereįor example, suppose you want to permute the following array along its second axis: Permutate this over axis 1 arr = np.arange(20).reshape((4, 5)) Or: np.random.shuffle(arr) # if you want to change the array in-place In practice, this can be achieved by using :len (data1) and len (data1): to slice permuteddata. Unlike many other numpy/random functions, () doesnt provide an obvious way to return multiple results in a single function call. If youre using numpy just for this, skip numpy altogether instead and just use random. Store the first len (data1) entries of permuteddata as permsample1 and the last len (data2) entries of permuteddata as permsample2. randomindices np.random.permutation(np.arange(len(a))) > aperm ai for i in randomindices. IMSPERBATCH indices np.arange(roundnumdata) npr.shuffle(indices.reshape(-1, cfg. np.random.shuffle (np.arange (n)) If x is an integer, randomly permute np.arange (x). To perform a permutation along the row axis of an array, you can use the following code: np.random.permutation(arr) # If you want to make a copy of the array Use np.random.permutation () to permute the concatenated array. Difference: np.random.permutation has two differences from np.random.shuffle: if passed an array, it will return a shuffled copy of the array np.random.shuffle shuffles the array inplace if passed an integer, it will return a shuffled range i.e.
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