69 lines
2 KiB
Python
69 lines
2 KiB
Python
import numpy as np
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import random
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import click
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import pathlib
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def evaluate(w, p):
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result = sum(map(lambda s: s[0] * s[1], zip(w, p)))
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return result
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@click.command()
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@click.argument('data_path')
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@click.argument('label_path')
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@click.argument('out_path')
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def generate_test_data(data_path: str, label_path: str, out_path: str):
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with open(data_path, "r") as f:
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desc = f.readline().strip()
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rows, dimensions = map(int, desc.split())
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data = np.loadtxt(f)
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print("loaded data")
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with open(label_path, "r") as f:
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desc = f.readline().strip()
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rows = int(desc)
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labels = np.loadtxt(f)
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print("loaded labels")
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indices = list(range(rows))
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random.shuffle(indices)
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split_at = int(0.7 * rows)
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train_indices = indices[:split_at]
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test_indices = indices[split_at:]
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# print("WTF?", train_indices, test_indices)
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train_data = data[train_indices,:]
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train_label = labels[train_indices]
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test_data = data[test_indices,:]
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test_label = labels[test_indices]
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out_path2 = pathlib.Path(out_path)
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out_path2.mkdir(exist_ok=True, parents=True)
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with open(out_path2 / "train_data.txt", "w") as f:
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f.write(f"{len(train_data)} {dimensions}\n")
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for row in train_data:
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for i, cell in enumerate(row):
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if i > 0: f.write(" ")
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f.write(str(cell))
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f.write("\n")
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with open(out_path2 / "test_data.txt", "w") as f:
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f.write(f"{len(test_data)} {dimensions}\n")
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for row in test_data:
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for i, cell in enumerate(row):
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if i > 0: f.write(" ")
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f.write(str(cell))
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f.write("\n")
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with open(out_path2 / "train_label.txt", "w") as f:
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f.write(f"{len(train_label)}\n")
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for cell in train_label:
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f.write(f"{cell}\n")
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with open(out_path2 / "test_label.txt", "w") as f:
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f.write(f"{len(test_label)}\n")
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for cell in test_label:
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f.write(f"{cell}\n")
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if __name__ == '__main__':
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generate_test_data()
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