NumPy np.random.permutation with seed?

NumPy np.random.permutation with seed?

In NumPy, you can use the np.random.permutation function to generate a random permutation of a sequence (array-like object). If you want to generate the same permutation sequence every time you run your code, you can set a seed for the random number generator using np.random.seed before calling np.random.permutation.

Here's an example of how to use np.random.permutation with a specified seed:

import numpy as np # Set the seed for reproducibility seed_value = 42 np.random.seed(seed_value) # Create an array or sequence to permute original_sequence = np.array([1, 2, 3, 4, 5]) # Generate a random permutation of the sequence random_permutation = np.random.permutation(original_sequence) print("Original sequence:", original_sequence) print("Random permutation:", random_permutation)

In this example, the np.random.seed(42) line sets the seed for the random number generator to 42. As a result, each time you run this code, you will get the same random permutation of the original sequence.

Keep in mind that setting a seed will ensure reproducibility. If you want different permutations for different runs of your code, you can omit the np.random.seed line, and each run will generate a different random permutation.

Remember that the seed value itself can be any integer, and using a consistent seed is important for reproducibility across different executions of your code.

Examples

  1. "How to use seed with NumPy np.random.permutation?"

    Description: Learn how to set a seed for reproducibility when using np.random.permutation() in NumPy.

    import numpy as np # Set seed for reproducibility np.random.seed(42) # Example usage of np.random.permutation() permuted_array = np.random.permutation(10)

  2. "Seed parameter in NumPy np.random.permutation()"

    Description: Understand the purpose and usage of the seed parameter in np.random.permutation().

    import numpy as np # Setting seed with np.random.permutation() permuted_array = np.random.permutation(10, seed=42)

  3. "Reproducible shuffling with NumPy np.random.permutation and seed"

    Description: Explore how to achieve consistent shuffling results using np.random.permutation() with a specified seed.

    import numpy as np # Set seed for reproducibility np.random.seed(42) # Shuffle an array reproducibly shuffled_array = np.random.permutation(np.arange(10))

  4. "NumPy permutation function with fixed seed"

    Description: Implement the np.random.permutation() function in NumPy with a fixed seed to obtain deterministic results.

    import numpy as np # Set seed for reproducibility np.random.seed(42) # Generate a permuted array with fixed seed permuted_array = np.random.permutation(10)

  5. "Ensuring reproducibility with NumPy permutation and seed"

    Description: Ensure reproducibility of permutation results using the seed parameter in NumPy's np.random.permutation() function.

    import numpy as np # Setting seed for reproducibility np.random.seed(42) # Generating a permuted array with a specified seed permuted_array = np.random.permutation(10)

  6. "Using seed for deterministic permutation in NumPy"

    Description: Utilize the seed parameter to obtain deterministic permutation results with NumPy's np.random.permutation() function.

    import numpy as np # Setting seed for reproducibility np.random.seed(42) # Perform permutation with a specified seed permuted_array = np.random.permutation(10)

  7. "NumPy permutation with fixed seed example"

    Description: View an example demonstrating the usage of a fixed seed with NumPy's np.random.permutation() function.

    import numpy as np # Set seed for reproducibility np.random.seed(42) # Example of permutation with a fixed seed permuted_array = np.random.permutation(10)

  8. "Setting seed for reproducible shuffling in NumPy"

    Description: Set a seed to ensure reproducible shuffling behavior when using np.random.permutation() in NumPy.

    import numpy as np # Set seed for reproducibility np.random.seed(42) # Perform reproducible shuffling shuffled_array = np.random.permutation(np.arange(10))

  9. "Generating reproducible random permutations with NumPy"

    Description: Generate random permutations reproducibly by setting a seed in NumPy's np.random.permutation() function.

    import numpy as np # Set seed for reproducibility np.random.seed(42) # Obtain reproducible random permutations permuted_array = np.random.permutation(10)

  10. "Controlling randomness with NumPy permutation and seed"

    Description: Control the randomness of permutations by specifying a seed when using NumPy's np.random.permutation() function.

    import numpy as np # Set seed for reproducibility np.random.seed(42) # Perform permutation with a specified seed permuted_array = np.random.permutation(10)

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