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zeekay
bde2e2531b
Machine Learning with FHE - fork of Zama Concrete-ML - Built-in FHE-friendly ML models - scikit-learn compatible API - PyTorch model conversion - Quantization-aware training
54 lines
1.1 KiB
Python
54 lines
1.1 KiB
Python
"""Tests for the torch to numpy module."""
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import random
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import numpy
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import pytest
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def body():
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"""Common function used in the tests, which picks random from numpy in different ways."""
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numpy.set_printoptions(threshold=10000)
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numpy.set_printoptions(linewidth=10000)
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print("Output: ", end="")
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# Python random
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for _ in range(1):
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print(random.randint(0, 1000), end="")
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# Numpy random
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for _ in range(1):
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print(numpy.random.randint(0, 1000), end="")
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print(numpy.random.uniform(-100, 100, size=(3, 3)).flatten(), end="")
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@pytest.mark.parametrize("parameters", [1, 2, 3])
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def test_bcm_seed_1(parameters):
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"""Test python and numpy seeding."""
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assert parameters is not None
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body()
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@pytest.mark.parametrize("parameters", [1, 3])
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def test_bcm_seed_2(parameters):
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"""Test python and numpy seeding."""
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assert parameters is not None
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body()
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@pytest.mark.parametrize("parameters", ["a", "b"])
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def test_bcm_seed_3(parameters):
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"""Test python and numpy seeding."""
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assert parameters is not None
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body()
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def test_bcm_seed_4():
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"""Test python and numpy seeding."""
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body()
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