27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
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import numpy as np
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from hypothesis import strategies as st
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MINIMUM_NETWORK_DIMENSION = 3
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def generate_kohonen_samples(num_features):
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"""Custom Hypothesis strategy to generate matrix X with consistent feature length."""
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return st.lists(
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st.lists(
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st.floats(allow_nan=False, allow_infinity=False, min_value=-1e6, max_value=1e6),
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min_size=num_features, max_size=num_features # Consistent feature length
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),
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min_size=1, max_size=100
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).map(lambda x: np.array(x, dtype=np.float32))
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def generate_kohonen_weights(width, height, feature_size):
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"""Custom Hypothesis strategy to generate a weights matrix with specified dimensions and feature size."""
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return st.lists(
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st.lists(
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st.lists(
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st.floats(min_value=-10, max_value=10),
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min_size=feature_size, max_size=feature_size # Each feature vector has a consistent feature size
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),
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min_size=width, max_size=width # Consistent width for each row
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),
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min_size=height, max_size=height # Consistent height for the matrix
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).map(lambda x: np.array(x, dtype=np.float32))
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