Move to separate repo

py/feed_forward.py

@@ -0,0 +1,48 @@
+import sys
+from pathlib import Path
+from timeit import timeit
+
+import numpy as np
+
+
+THIS_DIR = Path(__file__).parent
+TEST_DATA_DIR = Path(THIS_DIR, '..', 'data')
+
+
+def csv_to_array(file_path: Path, dtype=np.float32) -> np.ndarray:
+    return np.loadtxt(file_path, dtype=dtype, delimiter=',')
+
+
+def load_test_data() -> tuple[np.ndarray, list[tuple[np.ndarray, np.ndarray]]]:
+    inputs = csv_to_array(Path(TEST_DATA_DIR, 'inputs.csv'))
+    weights_iter = (csv_to_array(p) for p in sorted(TEST_DATA_DIR.glob('weights*.csv')))
+    biases_iter = (csv_to_array(p) for p in sorted(TEST_DATA_DIR.glob('biases*.csv')))
+    return inputs, list(zip(weights_iter, biases_iter))
+
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
+def layer_func(input_vector: np.ndarray, weight_matrix: np.ndarray, bias_vector: np.ndarray) -> np.ndarray:
+    return sigmoid(np.matmul(weight_matrix, input_vector) + bias_vector)
+
+
+def feed_forward(x: np.ndarray, *layers: tuple[np.ndarray, np.ndarray]) -> np.ndarray:
+    for w, b in layers:
+        x = layer_func(x, w, b)
+    return x
+
+
+def main(n):
+    t = timeit(
+        'feed_forward(inp, *layers)',
+        setup='from __main__ import feed_forward, load_test_data;' + 
+              'inp, layers = load_test_data()', 
+        number=n
+    )
+    print(round(t, 5))
+
+
+if __name__ == '__main__':
+    main(int(sys.argv[1]))

py/gen_data.py

@@ -0,0 +1,137 @@
+import sys
+from argparse import ArgumentParser
+from pathlib import Path
+from typing import Sequence
+
+import numpy as np
+
+
+THIS_DIR = Path(__file__).parent
+DEFAULT_DATA_DIR = Path(THIS_DIR, '..', 'data')
+
+INPUTS_FILE_NAME = 'inputs'
+WEIGHTS_FILE_NAME = 'weights'
+BIASES_FILE_NAME = 'biases'
+
+RNG = np.random.default_rng()
+
+
+def extension_with_dot(string: str) -> str:
+    string = string.strip()
+    if not string:
+        return string
+    return string if string.startswith('.') else '.' + string
+
+
+def parse_cli(args: list[str] = None) -> dict:
+    parser = ArgumentParser(description="Create test data files (input vector, weight matrices, and bias vectors).")
+    parser.add_argument(
+        'num_inputs',
+        type=int,
+        metavar='inputs',
+        help="Number of input dimensions. Random values are generated from a uniform distribution between 0.0 and 1.0."
+    )
+    parser.add_argument(
+        'num_neurons',
+        nargs='+',
+        type=int,
+        metavar='neurons',
+        help="Number of neurons in a layer. A weights file and a biases file will be created for each value passed. "
+             "Random values are generated from a uniform distribution between -1.0 and 1.0."
+    )
+    parser.add_argument(
+        '-d', '--directory',
+        type=Path,
+        default=DEFAULT_DATA_DIR,
+        help=f"Target directory to create the generated files in. Defaults to '{DEFAULT_DATA_DIR}'."
+    )
+    parser.add_argument(
+        '-e', '--file-extension',
+        type=extension_with_dot,
+        default='.csv',
+        help="File extension to use for the generated file names. Defaults to '.csv'."
+    )
+    parser.add_argument(
+        '--fmt',
+        default='%f',
+        help="Passed as the `fmt` parameter to numpy's `savetxt` function. Defaults to '%%f'. "
+             "(https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html)"
+    )
+    parser.add_argument(
+        '--delimiter',
+        default=',',
+        help="Passed as the `delimiter` parameter to numpy's `savetxt` function. Defaults to ','. "
+             "(https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html)"
+    )
+    parser.add_argument(
+        '-q', '--quiet',
+        action='store_true',
+        help="If this flag is set, no additional info is printed throughout the script."
+    )
+    parser.add_argument(
+        '-Y', '--yes',
+        action='store_true',
+        help="If this flag is set, confirmation is assumed throughout the script."
+    )
+    return vars(parser.parse_args(args))
+
+
+def prepare_directory(directory: Path, file_extension: str, yes: bool = False, quiet: bool = False) -> None:
+    directory.mkdir(exist_ok=True)
+    existing_files = list(directory.glob(f'*{file_extension}'))
+    if existing_files:
+        if yes:
+            delete = 'y'
+        else:
+            delete = input(f"{len(existing_files)} existing files with '{file_extension}' extension "
+                           f"found in {directory}. Delete these first? [Y/n] ").strip().lower() or 'y'
+        if delete == 'y':
+            for file_path in existing_files:
+                file_path.unlink()
+            if not quiet:
+                print("Deleted existing files.")
+        elif delete != 'n':
+            raise ValueError
+
+
+def generate_inputs(num_inputs: int, directory: Path, file_extension: str, quiet: bool = False, **kwargs) -> None:
+    inputs_file = Path(directory, INPUTS_FILE_NAME).with_suffix(file_extension)
+    input_vector = RNG.uniform(0.0, 1.0, size=num_inputs)
+    np.savetxt(inputs_file, input_vector, **kwargs)
+    if not quiet:
+        print(inputs_file, 'x'.join(str(n) for n in input_vector.shape))
+
+
+def generate_layers(num_inputs: int, num_neurons: Sequence[int], directory: Path, file_extension: str, 
+                    quiet: bool = False, **kwargs) -> None:
+    weights_file = Path(directory, WEIGHTS_FILE_NAME).with_suffix(file_extension)
+    biases_file = Path(directory, BIASES_FILE_NAME).with_suffix(file_extension)
+    dim_before = num_inputs
+    for i, dim in enumerate(num_neurons, start=1):
+        weight_matrix = RNG.uniform(-1.0, 1.0, size=(dim, dim_before))
+        bias_vector = RNG.uniform(-1.0, 1.0, size=dim)
+        weights_file = weights_file.with_stem(f'{WEIGHTS_FILE_NAME}{i:02}')
+        biases_file = biases_file.with_stem(f'{BIASES_FILE_NAME}{i:02}')
+        np.savetxt(weights_file, weight_matrix, **kwargs)
+        np.savetxt(biases_file, bias_vector, **kwargs)
+        if not quiet:
+            print(weights_file, 'x'.join(str(n) for n in weight_matrix.shape))
+            print(biases_file, 'x'.join(str(n) for n in bias_vector.shape))
+        dim_before = dim
+
+
+def generate_data(num_inputs: int, num_neurons: Sequence[int], directory: Path, file_extension: str, 
+                  quiet: bool = False, yes: bool = False, **kwargs) -> None:
+    prepare_directory(directory, file_extension, quiet, yes)
+    if not quiet:
+        print("Creating new test data...")
+    generate_inputs(num_inputs, directory, file_extension, quiet, **kwargs)
+    generate_layers(num_inputs, num_neurons, directory, file_extension, quiet, **kwargs)
+
+
+def main() -> None:
+    generate_data(**parse_cli())
+
+
+if __name__ == '__main__':
+    main()

py/requirements.txt

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+numpy