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]))