138 lines
5.1 KiB
Python
138 lines
5.1 KiB
Python
import sys
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from argparse import ArgumentParser
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from pathlib import Path
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from typing import Sequence
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import numpy as np
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THIS_DIR = Path(__file__).parent
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DEFAULT_DATA_DIR = Path(THIS_DIR, '..', 'data')
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INPUTS_FILE_NAME = 'inputs'
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WEIGHTS_FILE_NAME = 'weights'
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BIASES_FILE_NAME = 'biases'
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RNG = np.random.default_rng()
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def extension_with_dot(string: str) -> str:
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string = string.strip()
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if not string:
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return string
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return string if string.startswith('.') else '.' + string
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def parse_cli(args: list[str] = None) -> dict:
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parser = ArgumentParser(description="Create test data files (input vector, weight matrices, and bias vectors).")
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parser.add_argument(
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'num_inputs',
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type=int,
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metavar='inputs',
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help="Number of input dimensions. Random values are generated from a uniform distribution between 0.0 and 1.0."
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)
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parser.add_argument(
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'num_neurons',
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nargs='+',
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type=int,
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metavar='neurons',
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help="Number of neurons in a layer. A weights file and a biases file will be created for each value passed. "
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"Random values are generated from a uniform distribution between -1.0 and 1.0."
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)
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parser.add_argument(
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'-d', '--directory',
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type=Path,
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default=DEFAULT_DATA_DIR,
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help=f"Target directory to create the generated files in. Defaults to '{DEFAULT_DATA_DIR}'."
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)
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parser.add_argument(
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'-e', '--file-extension',
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type=extension_with_dot,
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default='.csv',
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help="File extension to use for the generated file names. Defaults to '.csv'."
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)
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parser.add_argument(
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'--fmt',
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default='%f',
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help="Passed as the `fmt` parameter to numpy's `savetxt` function. Defaults to '%%f'. "
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"(https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html)"
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)
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parser.add_argument(
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'--delimiter',
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default=',',
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help="Passed as the `delimiter` parameter to numpy's `savetxt` function. Defaults to ','. "
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"(https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html)"
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)
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parser.add_argument(
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'-q', '--quiet',
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action='store_true',
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help="If this flag is set, no additional info is printed throughout the script."
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)
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parser.add_argument(
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'-Y', '--yes',
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action='store_true',
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help="If this flag is set, confirmation is assumed throughout the script."
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)
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return vars(parser.parse_args(args))
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def prepare_directory(directory: Path, file_extension: str, yes: bool = False, quiet: bool = False) -> None:
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directory.mkdir(exist_ok=True)
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existing_files = list(directory.glob(f'*{file_extension}'))
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if existing_files:
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if yes:
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delete = 'y'
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else:
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delete = input(f"{len(existing_files)} existing files with '{file_extension}' extension "
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f"found in {directory}. Delete these first? [Y/n] ").strip().lower() or 'y'
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if delete == 'y':
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for file_path in existing_files:
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file_path.unlink()
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if not quiet:
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print("Deleted existing files.")
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elif delete != 'n':
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raise ValueError
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def generate_inputs(num_inputs: int, directory: Path, file_extension: str, quiet: bool = False, **kwargs) -> None:
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inputs_file = Path(directory, INPUTS_FILE_NAME).with_suffix(file_extension)
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input_vector = RNG.uniform(0.0, 1.0, size=num_inputs)
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np.savetxt(inputs_file, input_vector, **kwargs)
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if not quiet:
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print(inputs_file, 'x'.join(str(n) for n in input_vector.shape))
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def generate_layers(num_inputs: int, num_neurons: Sequence[int], directory: Path, file_extension: str,
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quiet: bool = False, **kwargs) -> None:
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weights_file = Path(directory, WEIGHTS_FILE_NAME).with_suffix(file_extension)
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biases_file = Path(directory, BIASES_FILE_NAME).with_suffix(file_extension)
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dim_before = num_inputs
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for i, dim in enumerate(num_neurons, start=1):
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weight_matrix = RNG.uniform(-1.0, 1.0, size=(dim, dim_before))
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bias_vector = RNG.uniform(-1.0, 1.0, size=dim)
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weights_file = weights_file.with_stem(f'{WEIGHTS_FILE_NAME}{i:02}')
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biases_file = biases_file.with_stem(f'{BIASES_FILE_NAME}{i:02}')
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np.savetxt(weights_file, weight_matrix, **kwargs)
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np.savetxt(biases_file, bias_vector, **kwargs)
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if not quiet:
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print(weights_file, 'x'.join(str(n) for n in weight_matrix.shape))
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print(biases_file, 'x'.join(str(n) for n in bias_vector.shape))
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dim_before = dim
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def generate_data(num_inputs: int, num_neurons: Sequence[int], directory: Path, file_extension: str,
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quiet: bool = False, yes: bool = False, **kwargs) -> None:
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prepare_directory(directory, file_extension, quiet, yes)
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if not quiet:
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print("Creating new test data...")
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generate_inputs(num_inputs, directory, file_extension, quiet, **kwargs)
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generate_layers(num_inputs, num_neurons, directory, file_extension, quiet, **kwargs)
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def main() -> None:
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generate_data(**parse_cli())
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if __name__ == '__main__':
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main()
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